Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation
With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (A...
Saved in:
| Published in | The Science of the total environment Vol. 627; pp. 78 - 94 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
15.06.2018
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0048-9697 1879-1026 1879-1026 |
| DOI | 10.1016/j.scitotenv.2018.01.202 |
Cover
| Abstract | With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes.
[Display omitted]
•Four different data-driven approaches are proposed for estimating daily carbon fluxes.•Long-term flux tower measurements from eight forest sites in different climates are used.•The effects of internal parameters on their corresponding methods are assessed together.•All models with proper functions can precisely simulate and predict carbon fluxes.•New ELM and ANFIS methods are highly recommended for estimating carbon fluxes. |
|---|---|
| AbstractList | With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes. With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes.With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes. With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes. [Display omitted] •Four different data-driven approaches are proposed for estimating daily carbon fluxes.•Long-term flux tower measurements from eight forest sites in different climates are used.•The effects of internal parameters on their corresponding methods are assessed together.•All models with proper functions can precisely simulate and predict carbon fluxes.•New ELM and ANFIS methods are highly recommended for estimating carbon fluxes. |
| Author | Yang, Yongguo Dou, Xianming |
| Author_xml | – sequence: 1 givenname: Xianming surname: Dou fullname: Dou, Xianming – sequence: 2 givenname: Yongguo orcidid: 0000-0002-9918-545X surname: Yang fullname: Yang, Yongguo email: yongguoyang@hotmail.com |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29426202$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkU9vFSEUxYmpsa_Vr6As3cwTeMw_ExdNU6tJjZvuCQOXyssMPIGZ2O_ih_W-Tu3CTWVzF_d3DnDOGTkJMQAh7zjbcsabD_ttNr7EAmHZCsa7LeM4xQuy4V3bV5yJ5oRsGJNd1Td9e0rOct4zPG3HX5FT0UvRIL8hv69y8ZMuPtxRFxPkQo1OQwzUjfMvyHTO62pO1HrnIEEo1OqiK5v8AoEWMD-C_zkjO-gMlqJ2jOGuKpAmCtbeUxMXnbwOBugEOs8JJnTJH-m3aGHE9XTAfUahDpbCoscZXxTDa_LS6THDm8d5Tm4_X91efqluvl9_vby4qYxkslR8kK5umeRcg8Y0jJEOagd962rDrGlq0YEerJS1ADfUdbODnrVMcGN3fNidk_er7SHF4z-Kmnw2MI46QJyzEmLX1rzHC55HGeOs6ZBF9O0jOg8TWHVImHO6V3-zR6BdAZNizgncE8KZOras9uqpZXVsWTGuVuWnf5SIPSRWkvbjf-gvVj1gqIuHdOQA67E-gSnKRv-sxx_a5M1f |
| CitedBy_id | crossref_primary_10_1038_s41598_024_64235_w crossref_primary_10_3390_ijerph192013127 crossref_primary_10_1155_2018_1824317 crossref_primary_10_1016_j_scitotenv_2021_145130 crossref_primary_10_1080_10106049_2021_1983032 crossref_primary_10_1016_j_geoderma_2018_11_044 crossref_primary_10_5194_bg_17_4421_2020 crossref_primary_10_1016_j_rsma_2019_100707 crossref_primary_10_1016_j_agrformet_2022_109036 crossref_primary_10_1016_j_scitotenv_2024_171182 crossref_primary_10_3389_feart_2022_848924 crossref_primary_10_3390_land12091710 crossref_primary_10_1007_s11356_021_16501_x crossref_primary_10_1016_j_agrformet_2023_109359 crossref_primary_10_1016_j_buildenv_2024_111878 crossref_primary_10_1016_j_ecolind_2021_107426 crossref_primary_10_1016_j_ecolind_2022_109845 crossref_primary_10_1016_j_jpba_2018_10_007 crossref_primary_10_3390_rs17010092 crossref_primary_10_3390_app12094770 crossref_primary_10_1016_j_rse_2019_111383 crossref_primary_10_1016_j_rse_2024_114072 crossref_primary_10_7717_peerj_8891 crossref_primary_10_1016_j_jenvman_2018_12_090 crossref_primary_10_1016_j_enconman_2021_114292 crossref_primary_10_3390_rs13122242 crossref_primary_10_3390_su12052099 crossref_primary_10_1016_j_apenergy_2020_114566 crossref_primary_10_1016_j_compag_2018_03_010 crossref_primary_10_1007_s00704_022_04265_4 crossref_primary_10_1007_s00704_022_04158_6 crossref_primary_10_3390_atmos15060727 crossref_primary_10_7717_peerj_16431 crossref_primary_10_1016_j_enbuild_2019_109377 crossref_primary_10_1029_2023JG007911 crossref_primary_10_3390_rs15030698 crossref_primary_10_1007_s00267_024_01965_7 crossref_primary_10_1016_j_plaphy_2023_107939 crossref_primary_10_3390_f13101721 crossref_primary_10_5194_bg_22_257_2025 crossref_primary_10_1007_s12665_018_7784_9 crossref_primary_10_1016_j_envres_2020_109262 crossref_primary_10_1061__ASCE_IR_1943_4774_0001471 |
| Cites_doi | 10.1111/gcb.12422 10.1016/j.jhydrol.2011.06.015 10.1016/j.envsoft.2010.02.003 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2 10.1109/TNN.2006.880583 10.1111/j.1469-8137.2007.01967.x 10.1016/j.agrformet.2012.11.002 10.1002/hyp.9669 10.1002/2015JG002997 10.1007/s11120-013-9925-z 10.1016/j.rser.2013.08.055 10.1111/gcb.12474 10.1016/S0168-1923(03)00023-6 10.1080/01431161.2017.1368098 10.1109/TSMC.1985.6313399 10.1177/0309133312444943 10.1007/s00477-015-1116-3 10.1016/j.energy.2016.02.021 10.1093/treephys/25.7.887 10.1080/02626667.2014.988155 10.1002/joc.3676 10.1029/2011JG001742 10.1007/s00704-015-1462-6 10.1016/j.scitotenv.2007.11.007 10.1016/j.agrformet.2010.08.003 10.1088/1748-9326/aa708b 10.1111/j.1600-0889.2007.00259.x 10.1007/s11269-015-1182-9 10.1890/12-0747.1 10.1007/s11063-012-9236-y 10.1002/2016GL068794 10.1111/j.1365-2486.2011.02562.x 10.1016/j.agrformet.2006.08.007 10.1016/j.neucom.2005.12.126 10.1016/j.neucom.2005.03.002 10.1016/j.agrformet.2010.04.008 10.1007/s11269-016-1480-x 10.5194/bg-10-8185-2013 10.1016/j.jhydrol.2016.02.012 10.1016/S0168-1923(96)02335-0 10.1007/s11269-012-0148-4 10.1016/j.ecolmodel.2005.03.014 10.1029/2007JD008965 10.1007/s10021-007-9018-y 10.1111/gcb.12916 10.1007/s13042-011-0019-y 10.1029/2012JG001960 10.1111/gcb.12766 10.1016/j.buildenv.2016.04.031 10.5194/bg-14-3445-2017 10.1109/21.256541 10.1111/gcb.13509 10.1016/j.jtherbio.2016.07.005 10.1016/j.enconman.2015.01.021 10.1016/j.ecolmodel.2012.07.017 10.1111/j.1365-2486.2006.01298.x 10.1016/S0168-1923(00)00235-5 10.1098/rspa.1998.0193 10.2166/nh.2013.112 10.1016/j.agrformet.2016.10.023 10.1016/j.applthermaleng.2015.10.140 10.1111/j.1365-2486.2006.01244.x 10.3390/f6061897 10.1016/j.jhydrol.2013.02.022 10.1016/j.compag.2015.08.020 10.1016/j.agrformet.2011.10.010 10.5194/acp-14-133-2014 10.1016/j.jhydrol.2010.12.030 10.1016/j.jhydrol.2016.09.035 10.1016/j.isprsjprs.2010.11.001 10.2166/nh.2011.020 10.1016/j.jhydrol.2013.11.021 10.5194/bg-5-969-2008 10.1016/S0168-1923(01)00244-1 10.1016/S0304-3800(02)00257-0 10.1016/j.foreco.2015.04.034 10.1016/j.ecolmodel.2009.11.007 10.1016/j.compgeo.2011.09.008 10.3390/f8120498 10.5194/gmd-6-2121-2013 10.1007/s00271-012-0332-6 10.1007/s11069-016-2540-5 10.1016/j.eswa.2006.07.007 10.1016/j.agrformet.2006.05.007 10.5194/gmd-6-45-2013 10.1029/2000GB001360 10.1007/s00521-013-1522-8 10.1016/j.jastp.2015.09.014 10.5194/bg-11-217-2014 10.1016/j.asoc.2015.02.011 10.1016/j.applthermaleng.2015.11.081 10.1016/j.agrformet.2007.08.011 10.1029/2001GL013607 10.5194/bg-12-1205-2015 10.1016/j.asoc.2014.02.002 10.1016/j.agrformet.2008.01.005 10.1046/j.1365-2486.1999.00281.x 10.1016/j.atmosenv.2014.11.006 10.3390/su10010203 10.1016/S1364-8152(98)00020-6 10.1016/j.cageo.2012.07.001 10.1002/hyp.7448 10.1061/(ASCE)IR.1943-4774.0000403 10.1007/s11269-011-9926-7 10.1046/j.1365-2486.2003.00609.x 10.1016/j.biombioe.2016.03.020 10.5194/bg-5-1625-2008 10.1016/j.neunet.2014.10.001 10.3808/jei.201300248 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier B.V. Copyright © 2018 Elsevier B.V. All rights reserved. |
| Copyright_xml | – notice: 2018 Elsevier B.V. – notice: Copyright © 2018 Elsevier B.V. All rights reserved. |
| DBID | AAYXX CITATION NPM 7X8 7S9 L.6 |
| DOI | 10.1016/j.scitotenv.2018.01.202 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Public Health Biology Environmental Sciences |
| EISSN | 1879-1026 |
| EndPage | 94 |
| ExternalDocumentID | 29426202 10_1016_j_scitotenv_2018_01_202 S0048969718302432 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9JM AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO ABFNM ABFYP ABJNI ABLST ABMAC ABYKQ ACDAQ ACGFS ACRLP ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGUBO AGYEJ AHEUO AHHHB AIEXJ AIKHN AITUG AJBFU AJOXV AKIFW ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BKOJK BLECG BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W K-O KCYFY KOM LY9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG RNS ROL RPZ SCU SDF SDG SDP SES SPCBC SSJ SSZ T5K ~02 ~G- ~KM 53G AAHBH AAQXK AATTM AAXKI AAYJJ AAYWO AAYXX ABEFU ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADMUD ADNMO ADXHL AEGFY AEIPS AEUPX AFJKZ AFPUW AGHFR AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB G-2 HMC HVGLF HZ~ R2- SEN SEW WUQ XPP ZXP ZY4 ~HD NPM SSH 7X8 7S9 L.6 |
| ID | FETCH-LOGICAL-c404t-1b4f570411aea201cc4fe5fe97f5c0dc6528eabd4452efb5563e907021cd31b3 |
| IEDL.DBID | .~1 |
| ISSN | 0048-9697 1879-1026 |
| IngestDate | Thu Oct 02 09:33:51 EDT 2025 Mon Sep 29 06:43:59 EDT 2025 Thu Apr 03 07:01:47 EDT 2025 Wed Oct 01 05:16:27 EDT 2025 Thu Apr 24 22:58:44 EDT 2025 Fri Feb 23 02:46:18 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Carbon fluxes Data-driven techniques Forest ecosystems Flux towers Extreme learning machine Adaptive neuro-fuzzy inference system |
| Language | English |
| License | Copyright © 2018 Elsevier B.V. All rights reserved. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c404t-1b4f570411aea201cc4fe5fe97f5c0dc6528eabd4452efb5563e907021cd31b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-9918-545X |
| PMID | 29426202 |
| PQID | 2001068904 |
| PQPubID | 23479 |
| PageCount | 17 |
| ParticipantIDs | proquest_miscellaneous_2237519041 proquest_miscellaneous_2001068904 pubmed_primary_29426202 crossref_primary_10_1016_j_scitotenv_2018_01_202 crossref_citationtrail_10_1016_j_scitotenv_2018_01_202 elsevier_sciencedirect_doi_10_1016_j_scitotenv_2018_01_202 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2018-06-15 |
| PublicationDateYYYYMMDD | 2018-06-15 |
| PublicationDate_xml | – month: 06 year: 2018 text: 2018-06-15 day: 15 |
| PublicationDecade | 2010 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | The Science of the total environment |
| PublicationTitleAlternate | Sci Total Environ |
| PublicationYear | 2018 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Abrahart, Anctil, Coulibaly, Dawson, Mount, See, Shamseldin, Solomatine, Toth, Wilby (bb0005) 2012; 36 Griffis, Black, Morgenstern, Barr, Nesic, Drewitt, Gaumont-Guay, McCaughey (bb0140) 2003; 117 Sanikhani, Kisi, Nikpour, Dinpashoh (bb0415) 2012; 26 Ding, Xu, Nie (bb0100) 2014; 25 Schaefer, Schwalm, Williams, Arain, Barr, Chen, Davis, Dimitrov, Hilton, Hollinger (bb0420) 2012; 117 Bastianelli, Ali, Beguin, Bergeron, Grondin, Hély, Paré (bb0035) 2017; 14 Schwalm, Black, Morgenstern, Humphreys (bb0430) 2007; 13 Dengel, Zona, Sachs, Aurela, Jammet, Parmentier, Oechel, Vesala (bb0090) 2013; 10 Jović, Arsić, Vilimonović, Petković (bb0225) 2016; 62 Thum, Aalto, Laurila, Aurela, Lindroth, Vesala (bb0505) 2008; 5 Keenan, Davidson, Munger, Richardson (bb0245) 2013; 23 Jensen, Herbst, Friborg (bb0220) 2017; 233 Huang, Wang, Lan (bb0195) 2011; 2 Cobaner (bb0075) 2011; 398 Richardson, Anderson, Arain, Barr, Bohrer, Chen, Chen, Ciais, Davis, Desai (bb0410) 2012; 18 Stoy, Katul, Siqueira, Juang, Novick, McCarthy, Christopher Oishi, Uebelherr, Kim, Oren (bb0485) 2006; 12 Shenify, Danesh, Gocić, Taher, Abdul Wahab, Gani, Shamshirband, Petković (bb0455) 2016; 30 Stoy, Katul, Siqueira, Juang, McCarthy, Kim, Oishi, Oren (bb0480) 2005; 25 Sharafi, Ebtehaj, Bonakdari, Zaji (bb0450) 2016; 84 Zha, Barr, van der Kamp, Black, McCaughey, Flanagan (bb0550) 2010; 150 Post, Hendricks Franssen, Graf, Schmidt, Vereecken (bb0385) 2014; 12 Frank, Reichstein, Bahn, Frank, Mahecha, Smith, Thonicke, Velde, Vicca, Babst (bb0130) 2015; 21 Kisi, Nia, Gosheh, Tajabadi, Ahmadi (bb0260) 2012; 26 Maier, Jain, Dandy, Sudheer (bb0315) 2010; 25 Huang, Huang, Song, You (bb0200) 2015; 61 Huang, Shen, Long, Wu, Shih, Zheng, Yen, Tung, Liu (bb0180) 1998; 454 Shiri, Shamshirband, Kisi, Karimi, Bateni, Nezhad, Hashemi (bb0475) 2016; 30 Raghavendra, Deka (bb0395) 2014; 19 Shiri, Marti, Nazemi, Sadraddini, Kisi, Landeras, Fard (bb0470) 2015; 46 Zounemat-Kermani, Kişi, Adamowski, Ramezani-Charmahineh (bb0555) 2016; 535 Kisi, Akbari, Sanatipour, Hashemi, Teimourzadeh, Shiri (bb0265) 2013; 22 Yadav, Chandel (bb0540) 2014; 33 Partal (bb0370) 2009; 23 Rannik, Kolari, Vesala, Hari (bb0400) 2006; 138 Cao, Lin, Huang (bb0055) 2012; 36 Kariminia, Motamedi, Shamshirband, Petković, Roy, Hashim (bb0230) 2016; 30 Napolitano, Serinaldi, See (bb0350) 2011; 406 Kurbatova, Li, Varlagin, Xiao, Vygodskaya (bb0285) 2008; 5 Barr, Morgenstern, Black, McCaughey, Nesic (bb0025) 2006; 140 Shiri, Dierickx, Pour-Ali Baba, Neamati, Ghorbani (bb0460) 2011; 42 Basser, Karami, Shamshirband, Akib, Amirmojahedi, Ahmad, Jahangirzadeh, Javidnia (bb0030) 2015; 30 Raos, Petković, Protić, Jovanović, Marković (bb0405) 2016; 104 Shamshirband, Tabatabaei, Aghbashlo, Yee, Petković (bb0445) 2016; 94 Huang, Chen, Wang (bb0190) 2007; 33 Wolf, Saliendra, Akshalov, Johnson, Laca (bb0530) 2008; 148 Dou, Chen, Black, Jassal, Che (bb0110) 2015; 6 Vahedi (bb0520) 2016; 88 Chang, Lin (bb0060) 2011; 2 Huang, Zhu, Siew (bb0185) 2006; 70 Dariane, Azimi (bb0080) 2016; 61 Cabalar, Cevik, Gokceoglu (bb0050) 2012; 40 Takagi, Sugeno (bb0495) 1985; SMC-15 Hosseini Nazhad, Lotfinejad, Danesh, ul Amin, Shamshirband (bb0175) 2017; 38 Mohammadi, Shamshirband, Petković, Yee, Mansor (bb0340) 2016; 96 Yaseen, Jaafar, Deo, Kisi, Adamowski, Quilty, El-Shafie (bb0545) 2016; 542 Groenendijk, Dolman, Ammann, Arneth, Cescatti, Dragoni, Gash, Gianelle, Gioli, Kiely (bb0145) 2011; 116 Lohani, Goel, Bhatia (bb0300) 2014; 509 Kişi, Ali Baba, Shiri (bb0255) 2011; 138 Luo, Keenan, Smith (bb0305) 2015; 21 Shiri, Marti, Singh (bb0465) 2014; 28 Shamshirband, Mohammadi, Chen, Narayana Samy, Petković, Ma (bb0440) 2015; 134 Tabari, Martinez, Ezani, Talaee (bb0490) 2013; 31 Menzer, Meiring, Kyriakidis, McFadden (bb0325) 2015; 101 Kisi, Shiri (bb0250) 2014; 34 Papale, Valentini (bb0360) 2003; 9 Li, Huang, Saratchandran, Sundararajan (bb0290) 2005; 68 Huntzinger, Schwalm, Michalak, Schaefer, King, Wei, Jacobson, Liu, Cook, Post (bb0205) 2013; 6 Thurner, Beer, Carvalhais, Forkel, Santoro, Tum, Schmullius (bb0510) 2016; 43 Citakoglu (bb0065) 2015; 118 Gundale, From, Bach, Nordin (bb0155) 2014; 20 Petković, Gocić, Shamshirband (bb0380) 2016; 14 Kisi, Shiri, Tombul (bb0270) 2013; 51 Schneising, Reuter, Buchwitz, Heymann, Bovensmann, Burrows (bb0425) 2014; 14 Schwartz, Uriarte, DeFries, Gutierrez-Velez, Pinedo-Vasquez (bb0435) 2017; 12 Dou, Yang (bb0105) 2017; 8 Hilton, Davis, Keller (bb0165) 2014; 11 Mountrakis, Im, Ogole (bb0345) 2011; 66 Papale, Black, Carvalhais, Cescatti, Chen, Jung, Kiely, Lasslop, Mahecha, Margolis (bb0365) 2015; 120 Delpierre, Soudani, François, Le Maire, Bernhofer, Kutsch, Misson, Rambal, Vesala, Dufrêne (bb0085) 2012; 154-155 Talei, Chua, Quek, Jansson (bb0500) 2013; 488 Noormets, Chen, Crow (bb0355) 2007; 10 Hollinger, Goltz, Davidson, Lee, Tu, Valentine (bb0170) 1999; 5 Liang, Huang, Saratchandran, Sundararajan (bb0295) 2006; 17 Botta, Ramankutty, Foley (bb0045) 2002; 29 Kariminia, Motamedi, Shamshirband, Piri, Mohammadi, Hashim, Roy, Petković, Bonakdari (bb0235) 2016; 124 Aubinet, Chermanne, Vandenhaute, Longdoz, Yernaux, Laitat (bb0010) 2001; 108 Peichl, Arain, Brodeur (bb0375) 2010; 150 Wu, Guan, Hayek, Restrepo-Coupe, Wiedemann, Xu, Wehr, Christoffersen, Miao, Silva (bb0535) 2017; 23 Maier, Dandy (bb0310) 1998; 13 Moffat, Papale, Reichstein, Hollinger, Richardson, Barr, Beckstein, Braswell, Churkina, Desai (bb0330) 2007; 147 Mohammadi, Shamshirband, Tong, Alam, Petković (bb0335) 2015; 93 Falge, Baldocchi, Olson, Anthoni, Aubinet, Bernhofer, Burba, Ceulemans, Clement, Dolman (bb0125) 2001; 107 Kuppel, Chevallier, Peylin (bb0280) 2013; 6 Evrendilek (bb0120) 2013; 171-172 Gevrey, Dimopoulos, Lek (bb0135) 2003; 160 Wang, Brender, Ciais, Piao, Mahecha, Chevallier, Reichstein, Ottlé, Maignan, Arain (bb0525) 2012; 246 Barman, Jain, Liang (bb0020) 2014; 20 Grünwald, Bernhofer (bb0150) 2007; 59 Hyvönen, Ågren, Linder, Persson, Cotrufo, Ekblad, Freeman, Grelle, Janssens, Jarvis (bb0210) 2007; 173 Qin, Su, Zhang, Ouyang, Yu, Li (bb0390) 2010; 221 Baldocchi, Vogel, Hall (bb0015) 1997; 83 Melesse, Hanley (bb0320) 2005; 189 Hilker, Coops, Wulder, Black, Guy (bb0160) 2008; 404 Jang (bb0215) 1993; 23 Dou, Yang, Luo (bb0115) 2018; 10 Clarke, Gundersen, Jönsson-Belyazid, Kjønaas, Persson, Sigurdsson, Stupak, Vesterdal (bb0070) 2015; 351 Torrence, Compo (bb0515) 1998; 79 Desai (bb0095) 2014; 119 Kariminia, Shamshirband, Hashim, Saberi, Petković, Roy, Motamedi (bb0240) 2016; 101 Krishnan, Black, Barr, Grant, Gaumont-Guay, Nesic (bb0275) 2008; 113 Bonan, Levis, Kergoat, Oleson (bb0040) 2002; 16 Jensen (10.1016/j.scitotenv.2018.01.202_bb0220) 2017; 233 Barman (10.1016/j.scitotenv.2018.01.202_bb0020) 2014; 20 Chang (10.1016/j.scitotenv.2018.01.202_bb0060) 2011; 2 Falge (10.1016/j.scitotenv.2018.01.202_bb0125) 2001; 107 Schwartz (10.1016/j.scitotenv.2018.01.202_bb0435) 2017; 12 Li (10.1016/j.scitotenv.2018.01.202_bb0290) 2005; 68 Vahedi (10.1016/j.scitotenv.2018.01.202_bb0520) 2016; 88 Jang (10.1016/j.scitotenv.2018.01.202_bb0215) 1993; 23 Shamshirband (10.1016/j.scitotenv.2018.01.202_bb0445) 2016; 94 Petković (10.1016/j.scitotenv.2018.01.202_bb0380) 2016; 14 Thurner (10.1016/j.scitotenv.2018.01.202_bb0510) 2016; 43 Abrahart (10.1016/j.scitotenv.2018.01.202_bb0005) 2012; 36 Kisi (10.1016/j.scitotenv.2018.01.202_bb0260) 2012; 26 Delpierre (10.1016/j.scitotenv.2018.01.202_bb0085) 2012; 154-155 Papale (10.1016/j.scitotenv.2018.01.202_bb0365) 2015; 120 Ding (10.1016/j.scitotenv.2018.01.202_bb0100) 2014; 25 Stoy (10.1016/j.scitotenv.2018.01.202_bb0480) 2005; 25 Qin (10.1016/j.scitotenv.2018.01.202_bb0390) 2010; 221 Huang (10.1016/j.scitotenv.2018.01.202_bb0185) 2006; 70 Cabalar (10.1016/j.scitotenv.2018.01.202_bb0050) 2012; 40 Maier (10.1016/j.scitotenv.2018.01.202_bb0315) 2010; 25 Takagi (10.1016/j.scitotenv.2018.01.202_bb0495) 1985; SMC-15 Yadav (10.1016/j.scitotenv.2018.01.202_bb0540) 2014; 33 Shenify (10.1016/j.scitotenv.2018.01.202_bb0455) 2016; 30 Bonan (10.1016/j.scitotenv.2018.01.202_bb0040) 2002; 16 Rannik (10.1016/j.scitotenv.2018.01.202_bb0400) 2006; 138 Hollinger (10.1016/j.scitotenv.2018.01.202_bb0170) 1999; 5 Shiri (10.1016/j.scitotenv.2018.01.202_bb0460) 2011; 42 Cobaner (10.1016/j.scitotenv.2018.01.202_bb0075) 2011; 398 Kisi (10.1016/j.scitotenv.2018.01.202_bb0250) 2014; 34 Huang (10.1016/j.scitotenv.2018.01.202_bb0180) 1998; 454 Kariminia (10.1016/j.scitotenv.2018.01.202_bb0230) 2016; 30 Stoy (10.1016/j.scitotenv.2018.01.202_bb0485) 2006; 12 Schwalm (10.1016/j.scitotenv.2018.01.202_bb0430) 2007; 13 Yaseen (10.1016/j.scitotenv.2018.01.202_bb0545) 2016; 542 Dou (10.1016/j.scitotenv.2018.01.202_bb0110) 2015; 6 Sharafi (10.1016/j.scitotenv.2018.01.202_bb0450) 2016; 84 Peichl (10.1016/j.scitotenv.2018.01.202_bb0375) 2010; 150 Zounemat-Kermani (10.1016/j.scitotenv.2018.01.202_bb0555) 2016; 535 Kariminia (10.1016/j.scitotenv.2018.01.202_bb0235) 2016; 124 Menzer (10.1016/j.scitotenv.2018.01.202_bb0325) 2015; 101 Talei (10.1016/j.scitotenv.2018.01.202_bb0500) 2013; 488 Dou (10.1016/j.scitotenv.2018.01.202_bb0115) 2018; 10 Shiri (10.1016/j.scitotenv.2018.01.202_bb0475) 2016; 30 Krishnan (10.1016/j.scitotenv.2018.01.202_bb0275) 2008; 113 Schaefer (10.1016/j.scitotenv.2018.01.202_bb0420) 2012; 117 Napolitano (10.1016/j.scitotenv.2018.01.202_bb0350) 2011; 406 Griffis (10.1016/j.scitotenv.2018.01.202_bb0140) 2003; 117 Dou (10.1016/j.scitotenv.2018.01.202_bb0105) 2017; 8 Tabari (10.1016/j.scitotenv.2018.01.202_bb0490) 2013; 31 Hilker (10.1016/j.scitotenv.2018.01.202_bb0160) 2008; 404 Dengel (10.1016/j.scitotenv.2018.01.202_bb0090) 2013; 10 Hilton (10.1016/j.scitotenv.2018.01.202_bb0165) 2014; 11 Schneising (10.1016/j.scitotenv.2018.01.202_bb0425) 2014; 14 Raghavendra (10.1016/j.scitotenv.2018.01.202_bb0395) 2014; 19 Gundale (10.1016/j.scitotenv.2018.01.202_bb0155) 2014; 20 Thum (10.1016/j.scitotenv.2018.01.202_bb0505) 2008; 5 Baldocchi (10.1016/j.scitotenv.2018.01.202_bb0015) 1997; 83 Kurbatova (10.1016/j.scitotenv.2018.01.202_bb0285) 2008; 5 Aubinet (10.1016/j.scitotenv.2018.01.202_bb0010) 2001; 108 Cao (10.1016/j.scitotenv.2018.01.202_bb0055) 2012; 36 Dariane (10.1016/j.scitotenv.2018.01.202_bb0080) 2016; 61 Mohammadi (10.1016/j.scitotenv.2018.01.202_bb0340) 2016; 96 Lohani (10.1016/j.scitotenv.2018.01.202_bb0300) 2014; 509 Huang (10.1016/j.scitotenv.2018.01.202_bb0195) 2011; 2 Keenan (10.1016/j.scitotenv.2018.01.202_bb0245) 2013; 23 Moffat (10.1016/j.scitotenv.2018.01.202_bb0330) 2007; 147 Maier (10.1016/j.scitotenv.2018.01.202_bb0310) 1998; 13 Evrendilek (10.1016/j.scitotenv.2018.01.202_bb0120) 2013; 171-172 Kisi (10.1016/j.scitotenv.2018.01.202_bb0270) 2013; 51 Richardson (10.1016/j.scitotenv.2018.01.202_bb0410) 2012; 18 Clarke (10.1016/j.scitotenv.2018.01.202_bb0070) 2015; 351 Botta (10.1016/j.scitotenv.2018.01.202_bb0045) 2002; 29 Gevrey (10.1016/j.scitotenv.2018.01.202_bb0135) 2003; 160 Kisi (10.1016/j.scitotenv.2018.01.202_bb0265) 2013; 22 Huntzinger (10.1016/j.scitotenv.2018.01.202_bb0205) 2013; 6 Kişi (10.1016/j.scitotenv.2018.01.202_bb0255) 2011; 138 Melesse (10.1016/j.scitotenv.2018.01.202_bb0320) 2005; 189 Mountrakis (10.1016/j.scitotenv.2018.01.202_bb0345) 2011; 66 Sanikhani (10.1016/j.scitotenv.2018.01.202_bb0415) 2012; 26 Frank (10.1016/j.scitotenv.2018.01.202_bb0130) 2015; 21 Liang (10.1016/j.scitotenv.2018.01.202_bb0295) 2006; 17 Wolf (10.1016/j.scitotenv.2018.01.202_bb0530) 2008; 148 Huang (10.1016/j.scitotenv.2018.01.202_bb0200) 2015; 61 Hosseini Nazhad (10.1016/j.scitotenv.2018.01.202_bb0175) 2017; 38 Papale (10.1016/j.scitotenv.2018.01.202_bb0360) 2003; 9 Luo (10.1016/j.scitotenv.2018.01.202_bb0305) 2015; 21 Partal (10.1016/j.scitotenv.2018.01.202_bb0370) 2009; 23 Shiri (10.1016/j.scitotenv.2018.01.202_bb0470) 2015; 46 Torrence (10.1016/j.scitotenv.2018.01.202_bb0515) 1998; 79 Bastianelli (10.1016/j.scitotenv.2018.01.202_bb0035) 2017; 14 Jović (10.1016/j.scitotenv.2018.01.202_bb0225) 2016; 62 Zha (10.1016/j.scitotenv.2018.01.202_bb0550) 2010; 150 Grünwald (10.1016/j.scitotenv.2018.01.202_bb0150) 2007; 59 Mohammadi (10.1016/j.scitotenv.2018.01.202_bb0335) 2015; 93 Huang (10.1016/j.scitotenv.2018.01.202_bb0190) 2007; 33 Groenendijk (10.1016/j.scitotenv.2018.01.202_bb0145) 2011; 116 Hyvönen (10.1016/j.scitotenv.2018.01.202_bb0210) 2007; 173 Kariminia (10.1016/j.scitotenv.2018.01.202_bb0240) 2016; 101 Kuppel (10.1016/j.scitotenv.2018.01.202_bb0280) 2013; 6 Basser (10.1016/j.scitotenv.2018.01.202_bb0030) 2015; 30 Shiri (10.1016/j.scitotenv.2018.01.202_bb0465) 2014; 28 Citakoglu (10.1016/j.scitotenv.2018.01.202_bb0065) 2015; 118 Wu (10.1016/j.scitotenv.2018.01.202_bb0535) 2017; 23 Post (10.1016/j.scitotenv.2018.01.202_bb0385) 2014; 12 Raos (10.1016/j.scitotenv.2018.01.202_bb0405) 2016; 104 Noormets (10.1016/j.scitotenv.2018.01.202_bb0355) 2007; 10 Desai (10.1016/j.scitotenv.2018.01.202_bb0095) 2014; 119 Shamshirband (10.1016/j.scitotenv.2018.01.202_bb0440) 2015; 134 Wang (10.1016/j.scitotenv.2018.01.202_bb0525) 2012; 246 Barr (10.1016/j.scitotenv.2018.01.202_bb0025) 2006; 140 |
| References_xml | – volume: 6 start-page: 2121 year: 2013 end-page: 2133 ident: bb0205 article-title: The north American carbon program multi-scale synthesis and terrestrial model Intercomparison project-part 1: overview and experimental design publication-title: Geosci. Model Dev. – volume: 36 start-page: 480 year: 2012 end-page: 513 ident: bb0005 article-title: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting publication-title: Prog. Phys. Geogr. – volume: 351 start-page: 9 year: 2015 end-page: 19 ident: bb0070 article-title: Influence of different tree-harvesting intensities on forest soil carbon stocks in boreal and northern temperate forest ecosystems publication-title: For. Ecol. Manag. – volume: 59 start-page: 387 year: 2007 end-page: 396 ident: bb0150 article-title: A decade of carbon, water and energy flux measurements of an old spruce forest at the Anchor Station Tharandt publication-title: Tellus Ser. B Chem. Phys. Meteorol. – volume: 30 start-page: 642 year: 2015 end-page: 649 ident: bb0030 article-title: Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike publication-title: Appl. Soft Comput. – volume: 18 start-page: 566 year: 2012 end-page: 584 ident: bb0410 article-title: Terrestrial biosphere models need better representation of vegetation phenology: results from the north American carbon program site synthesis publication-title: Glob. Chang. Biol. – volume: 31 start-page: 575 year: 2013 end-page: 588 ident: bb0490 article-title: Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration publication-title: Irrig. Sci. – volume: 14 start-page: 3445 year: 2017 end-page: 3459 ident: bb0035 article-title: Boreal coniferous forest density leads to significant variations in soil physical and geochemical properties publication-title: Biogeosciences – volume: 30 start-page: 1189 year: 2016 end-page: 1203 ident: bb0230 article-title: Adaptation of ANFIS model to assess thermal comfort of an urban square in moderate and dry climate publication-title: Stoch. Env. Res. Risk A. – volume: 51 start-page: 108 year: 2013 end-page: 117 ident: bb0270 article-title: Modeling rainfall-runoff process using soft computing techniques publication-title: Computer. Geosci. U. K. – volume: 61 start-page: 585 year: 2016 end-page: 600 ident: bb0080 article-title: Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models publication-title: Hydrol. Sci. J. – volume: 66 start-page: 247 year: 2011 end-page: 259 ident: bb0345 article-title: Support vector machines in remote sensing: a review publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 20 start-page: 276 year: 2014 end-page: 286 ident: bb0155 article-title: Anthropogenic nitrogen deposition in boreal forests has a minor impact on the global carbon cycle publication-title: Glob. Chang. Biol. – volume: 118 start-page: 28 year: 2015 end-page: 37 ident: bb0065 article-title: Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation publication-title: Comput. Electron. Agric. – volume: 119 start-page: 31 year: 2014 end-page: 47 ident: bb0095 article-title: Influence and predictive capacity of climate anomalies on daily to decadal extremes in canopy photosynthesis publication-title: Photosynth. Res. – volume: 23 start-page: 273 year: 2013 end-page: 286 ident: bb0245 article-title: Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle publication-title: Ecol. Appl. – volume: 6 start-page: 1897 year: 2015 end-page: 1921 ident: bb0110 article-title: Impact of nitrogen fertilization on forest carbon sequestration and water loss in a chronosequence of three Douglas-fir stands in the Pacific Northwest publication-title: Forests – volume: 9 start-page: 525 year: 2003 end-page: 535 ident: bb0360 article-title: A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization publication-title: Glob. Chang. Biol. – volume: 14 start-page: 133 year: 2014 end-page: 141 ident: bb0425 article-title: Terrestrial carbon sink observed from space: variation of growth rates and seasonal cycle amplitudes in response to interannual surface temperature variability publication-title: Atmos. Chem. Phys. – volume: 233 start-page: 12 year: 2017 end-page: 31 ident: bb0220 article-title: Direct and indirect controls of the interannual variability in atmospheric CO publication-title: Agric. For. Meteorol. – volume: 221 start-page: 575 year: 2010 end-page: 581 ident: bb0390 article-title: Identification of important factors for water vapor flux and CO publication-title: Ecol. Model. – volume: 8 start-page: 498 year: 2017 ident: bb0105 article-title: Modeling and predicting carbon and water fluxes using data-driven techniques in a forest ecosystem publication-title: Forests – volume: 23 start-page: 3545 year: 2009 end-page: 3555 ident: bb0370 article-title: Modelling evapotranspiration using discrete wavelet transform and neural networks publication-title: Hydrol. Process. – volume: 17 start-page: 1411 year: 2006 end-page: 1423 ident: bb0295 article-title: A fast and accurate online sequential learning algorithm for feedforward networks publication-title: IEEE Trans. Neural Netw. – volume: 68 start-page: 306 year: 2005 end-page: 314 ident: bb0290 article-title: Fully complex extreme learning machine publication-title: Neurocomputing – volume: 147 start-page: 209 year: 2007 end-page: 232 ident: bb0330 article-title: Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes publication-title: Agric. For. Meteorol. – volume: 173 start-page: 463 year: 2007 end-page: 480 ident: bb0210 article-title: The likely impact of elevated [CO publication-title: New Phytol. – volume: 26 start-page: 457 year: 2012 end-page: 474 ident: bb0260 article-title: Intermittent streamflow forecasting by using several data driven techniques publication-title: Water Resour. Manag. – volume: 61 start-page: 32 year: 2015 end-page: 48 ident: bb0200 article-title: Trends in extreme learning machines: a review publication-title: Neural Netw. – volume: 13 start-page: 370 year: 2007 end-page: 385 ident: bb0430 article-title: A method for deriving net primary productivity and component respiratory fluxes from tower-based eddy covariance data: a case study using a 17-year data record from a Douglas-fir chronosequence publication-title: Glob. Chang. Biol. – volume: 124 start-page: 991 year: 2016 end-page: 1004 ident: bb0235 article-title: Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method publication-title: Theor. Appl. Climatol. – volume: 189 start-page: 305 year: 2005 end-page: 314 ident: bb0320 article-title: Artificial neural network application for multi-ecosystem carbon flux simulation publication-title: Ecol. Model. – volume: 40 start-page: 14 year: 2012 end-page: 33 ident: bb0050 article-title: Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering publication-title: Comput. Geotech. – volume: 5 start-page: 1625 year: 2008 end-page: 1639 ident: bb0505 article-title: Assessing seasonality of biochemical CO publication-title: Biogeosciences – volume: SMC-15 start-page: 116 year: 1985 end-page: 132 ident: bb0495 article-title: Fuzzy identification of systems and its applications to modeling and control publication-title: IEEE Trans. Syst. Man Cybern. – volume: 535 start-page: 457 year: 2016 end-page: 472 ident: bb0555 article-title: Evaluation of data driven models for river suspended sediment concentration modeling publication-title: J. Hydrol. – volume: 16 start-page: 1021 year: 2002 ident: bb0040 article-title: Landscapes as patches of plant functional types: an integrating concept for climate and ecosystem models publication-title: Glob. Biogeochem. Cycles – volume: 36 start-page: 285 year: 2012 end-page: 305 ident: bb0055 article-title: Self-adaptive evolutionary extreme learning machine publication-title: Neural. Process. Lett. – volume: 79 start-page: 61 year: 1998 end-page: 78 ident: bb0515 article-title: A practical guide to wavelet analysis publication-title: Bull. Am. Meteorol. Soc. – volume: 154-155 start-page: 99 year: 2012 end-page: 112 ident: bb0085 article-title: Quantifying the influence of climate and biological drivers on the interannual variability of carbon exchanges in European forests through process-based modelling publication-title: Agric. For. Meteorol. – volume: 113 year: 2008 ident: bb0275 article-title: Factors controlling the interannual variability in the carbon balance of a southern boreal black spruce forest publication-title: J. Geophys. Res. – volume: 150 start-page: 1476 year: 2010 end-page: 1484 ident: bb0550 article-title: Interannual variation of evapotranspiration from forest and grassland ecosystems in western Canada in relation to drought publication-title: Agric. For. Meteorol. – volume: 246 start-page: 11 year: 2012 end-page: 25 ident: bb0525 article-title: State-dependent errors in a land surface model across biomes inferred from eddy covariance observations on multiple timescales publication-title: Ecol. Model. – volume: 160 start-page: 249 year: 2003 end-page: 264 ident: bb0135 article-title: Review and comparison of methods to study the contribution of variables in artificial neural network models publication-title: Ecol. Model. – volume: 11 start-page: 217 year: 2014 end-page: 235 ident: bb0165 article-title: Evaluating terrestrial CO publication-title: Biogeosciences – volume: 104 start-page: 68 year: 2016 end-page: 75 ident: bb0405 article-title: Selection of the most influential flow and thermal parameters for predicting the efficiency of activated carbon filters using neuro-fuzzy technique publication-title: Build. Environ. – volume: 101 start-page: 568 year: 2016 end-page: 580 ident: bb0240 article-title: A simulation model for visitors' thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies publication-title: Energy – volume: 138 start-page: 244 year: 2006 end-page: 257 ident: bb0400 article-title: Uncertainties in measurement and modelling of net ecosystem exchange of a forest publication-title: Agric. For. Meteorol. – volume: 12 start-page: 1205 year: 2014 end-page: 1221 ident: bb0385 article-title: Uncertainty analysis of eddy covariance CO publication-title: Biogeosciences – volume: 117 start-page: 53 year: 2003 end-page: 71 ident: bb0140 article-title: Ecophysiological controls on the carbon balances of three southern boreal forests publication-title: Agric. For. Meteorol. – volume: 23 start-page: 665 year: 1993 end-page: 685 ident: bb0215 article-title: ANFIS: adaptive-network-based fuzzy inference system publication-title: IEEE Trans. Syst. Man Cybern. – volume: 107 start-page: 71 year: 2001 end-page: 77 ident: bb0125 article-title: Gap filling strategies for long term energy flux data sets publication-title: Agric. For. Meteorol. – volume: 116 year: 2011 ident: bb0145 article-title: Seasonal variation of photosynthetic model parameters and leaf area index from global Fluxnet eddy covariance data publication-title: J. Geophys. Res. Biogeosci. – volume: 94 start-page: 727 year: 2016 end-page: 747 ident: bb0445 article-title: Support vector machine-based exergetic modelling of a DI diesel engine running on biodiesel–diesel blends containing expanded polystyrene publication-title: Appl. Therm. Eng. – volume: 542 start-page: 603 year: 2016 end-page: 614 ident: bb0545 article-title: Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq publication-title: J. Hydrol. – volume: 138 start-page: 349 year: 2011 end-page: 362 ident: bb0255 article-title: Generalized neurofuzzy models for estimating daily pan evaporation values from weather data publication-title: J. Irrig. Drain. Eng. – volume: 117 year: 2012 ident: bb0420 article-title: A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis publication-title: J. Geophys. Res.-Biogeosci. – volume: 88 start-page: 66 year: 2016 end-page: 76 ident: bb0520 article-title: Artificial neural network application in comparison with modeling allometric equations for predicting above-ground biomass in the Hyrcanian mixed-beech forests of Iran publication-title: Biomass Bioenergy – volume: 29 start-page: 33 year: 2002 end-page: 1-33-4 ident: bb0045 article-title: Long-term variations of climate and carbon fluxes over the Amazon basin publication-title: Geophys. Res. Lett. – volume: 150 start-page: 1090 year: 2010 end-page: 1101 ident: bb0375 article-title: Age effects on carbon fluxes in temperate pine forests publication-title: Agric. For. Meteorol. – volume: 22 start-page: 92 year: 2013 end-page: 101 ident: bb0265 article-title: Modeling of dissolved oxygen in river water using artificial intelligence techniques publication-title: J. Environ. Inform. – volume: 148 start-page: 942 year: 2008 end-page: 952 ident: bb0530 article-title: Effects of different eddy covariance correction schemes on energy balance closure and comparisons with the modified Bowen ratio system publication-title: Agric. For. Meteorol. – volume: 38 start-page: 6894 year: 2017 end-page: 6909 ident: bb0175 article-title: A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran publication-title: Int. J. Remote Sens. – volume: 12 year: 2017 ident: bb0435 article-title: Land-use dynamics influence estimates of carbon sequestration potential in tropical second-growth forest publication-title: Environ. Res. Lett. – volume: 140 start-page: 322 year: 2006 end-page: 337 ident: bb0025 article-title: Surface energy balance closure by the eddy-covariance method above three boreal forest stands and implications for the measurement of the CO publication-title: Agric. For. Meteorol. – volume: 34 start-page: 179 year: 2014 end-page: 186 ident: bb0250 article-title: Prediction of long-term monthly air temperature using geographical inputs publication-title: Int. J. Climatol. – volume: 43 start-page: 4576 year: 2016 end-page: 4585 ident: bb0510 article-title: Large–scale variation in boreal and temperate forest carbon turnover rate related to climate publication-title: Geophys. Res. Lett. – volume: 25 start-page: 887 year: 2005 end-page: 902 ident: bb0480 article-title: Variability in net ecosystem exchange from hourly to inter-annual time scales at adjacent pine and hardwood forests: a wavelet analysis publication-title: Tree Physiol. – volume: 25 start-page: 549 year: 2014 end-page: 556 ident: bb0100 article-title: Extreme learning machine and its applications publication-title: Neural Comput. & Applic. – volume: 26 start-page: 4347 year: 2012 end-page: 4365 ident: bb0415 article-title: Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques publication-title: Water Resour. Manag. – volume: 134 start-page: 109 year: 2015 end-page: 117 ident: bb0440 article-title: Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: a case study for Iran publication-title: J. Atmos. Sol. Terr. Phys. – volume: 404 start-page: 411 year: 2008 end-page: 423 ident: bb0160 article-title: The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements publication-title: Sci. Total Environ. – volume: 398 start-page: 292 year: 2011 end-page: 302 ident: bb0075 article-title: Evapotranspiration estimation by two different neuro-fuzzy inference systems publication-title: J. Hydrol. – volume: 10 start-page: 203 year: 2018 ident: bb0115 article-title: Estimating forest carbon fluxes using machine learning techniques based on eddy covariance measurements publication-title: Sustainability – volume: 70 start-page: 489 year: 2006 end-page: 501 ident: bb0185 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing – volume: 12 start-page: 2115 year: 2006 end-page: 2135 ident: bb0485 article-title: Separating the effects of climate and vegetation on evapotranspiration along a successional chronosequence in the southeastern US publication-title: Glob. Chang. Biol. – volume: 14 start-page: 209 year: 2016 end-page: 218 ident: bb0380 article-title: Adaptive neuro-fuzzy computing technique for precipitation estimation publication-title: Series: Mechanical Engineering – volume: 509 start-page: 25 year: 2014 end-page: 41 ident: bb0300 article-title: Improving real time flood forecasting using fuzzy inference system publication-title: J. Hydrol. – volume: 108 start-page: 293 year: 2001 end-page: 315 ident: bb0010 article-title: Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes publication-title: Agric. For. Meteorol. – volume: 96 start-page: 311 year: 2016 end-page: 319 ident: bb0340 article-title: Using ANFIS for selection of more relevant parameters to predict dew point temperature publication-title: Appl. Therm. Eng. – volume: 84 start-page: 2145 year: 2016 end-page: 2162 ident: bb0450 article-title: Design of a support vector machine with different kernel functions to predict scour depth around bridge piers publication-title: Nat. Hazards – volume: 406 start-page: 199 year: 2011 end-page: 214 ident: bb0350 article-title: Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: an empirical examination publication-title: J. Hydrol. – volume: 33 start-page: 847 year: 2007 end-page: 856 ident: bb0190 article-title: Credit scoring with a data mining approach based on support vector machines publication-title: Expert Syst. Appl. – volume: 23 start-page: 1240 year: 2017 end-page: 1257 ident: bb0535 article-title: Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales publication-title: Glob. Chang. Biol. – volume: 30 start-page: 641 year: 2016 end-page: 652 ident: bb0455 article-title: Precipitation estimation using support vector machine with discrete wavelet transform publication-title: Water Resour. Manag. – volume: 42 start-page: 491 year: 2011 ident: bb0460 article-title: Estimating daily pan evaporation from climatic data of the state of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) publication-title: Hydrol. Res. – volume: 5 start-page: 891 year: 1999 end-page: 902 ident: bb0170 article-title: Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest publication-title: Glob. Chang. Biol. – volume: 21 start-page: 2861 year: 2015 end-page: 2880 ident: bb0130 article-title: Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts publication-title: Glob. Chang. Biol. – volume: 93 start-page: 406 year: 2015 end-page: 413 ident: bb0335 article-title: Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year publication-title: Energy Convers. Manag. – volume: 488 start-page: 17 year: 2013 end-page: 32 ident: bb0500 article-title: Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning publication-title: J. Hydrol. – volume: 5 start-page: 969 year: 2008 end-page: 980 ident: bb0285 article-title: Modeling carbon dynamics in two adjacent spruce forests with different soil conditions in Russia publication-title: Biogeosciences – volume: 83 start-page: 147 year: 1997 end-page: 170 ident: bb0015 article-title: Seasonal variation of carbon dioxide exchange rates above and below a boreal jack pine forest publication-title: Agric. For. Meteorol. – volume: 25 start-page: 891 year: 2010 end-page: 909 ident: bb0315 article-title: Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions publication-title: Environ. Model. Softw. – volume: 30 start-page: 5217 year: 2016 end-page: 5229 ident: bb0475 article-title: Prediction of water-level in the Urmia Lake using the extreme learning machine approach publication-title: Water Resour. Manag. – volume: 46 start-page: 72 year: 2015 end-page: 88 ident: bb0470 article-title: Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing publication-title: Hydrol. Res. – volume: 2 start-page: 27 year: 2011 ident: bb0060 article-title: LIBSVM: A Library for Support Vector Machines – volume: 101 start-page: 312 year: 2015 end-page: 327 ident: bb0325 article-title: Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling publication-title: Atmos. Environ. – volume: 120 start-page: 1941 year: 2015 end-page: 1957 ident: bb0365 article-title: Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks publication-title: J. Geophys. Res. Biogeosci. – volume: 10 start-page: 187 year: 2007 end-page: 203 ident: bb0355 article-title: Age-dependent changes in ecosystem carbon fluxes in managed forests in northern Wisconsin, USA publication-title: Ecosystems – volume: 6 start-page: 45 year: 2013 end-page: 55 ident: bb0280 article-title: Quantifying the model structural error in carbon cycle data assimilation systems publication-title: Geosci. Model Dev. – volume: 20 start-page: 1394 year: 2014 end-page: 1411 ident: bb0020 article-title: Climate-driven uncertainties in modeling terrestrial gross primary production: a site level to global-scale analysis publication-title: Glob. Chang. Biol. – volume: 33 start-page: 772 year: 2014 end-page: 781 ident: bb0540 article-title: Solar radiation prediction using artificial neural network techniques: a review publication-title: Renew. Sust. Energ. Rev. – volume: 10 start-page: 8185 year: 2013 end-page: 8200 ident: bb0090 article-title: Testing the applicability of neural networks as a gap-filling method using CH publication-title: Biogeosciences – volume: 21 start-page: 1737 year: 2015 end-page: 1751 ident: bb0305 article-title: Predictability of the terrestrial carbon cycle publication-title: Glob. Chang. Biol. – volume: 454 start-page: 903 year: 1998 end-page: 995 ident: bb0180 article-title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis publication-title: Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. – volume: 62 start-page: 106 year: 2016 end-page: 108 ident: bb0225 article-title: Thermal sensation prediction by soft computing methodology publication-title: J. Therm. Biol. – volume: 2 start-page: 107 year: 2011 end-page: 122 ident: bb0195 article-title: Extreme learning machines: a survey publication-title: Int. J. Mach. Learn. Cybern. – volume: 13 start-page: 193 year: 1998 end-page: 209 ident: bb0310 article-title: The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study publication-title: Environ. Model. Softw. – volume: 28 start-page: 1215 year: 2014 end-page: 1225 ident: bb0465 article-title: Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning publication-title: Hydrol. Process. – volume: 19 start-page: 372 year: 2014 end-page: 386 ident: bb0395 article-title: Support vector machine applications in the field of hydrology: a review publication-title: Appl. Soft Comput. – volume: 171-172 start-page: 1 year: 2013 end-page: 8 ident: bb0120 article-title: Quantifying biosphere–atmosphere exchange of CO publication-title: Agric. For. Meteorol. – volume: 20 start-page: 276 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0155 article-title: Anthropogenic nitrogen deposition in boreal forests has a minor impact on the global carbon cycle publication-title: Glob. Chang. Biol. doi: 10.1111/gcb.12422 – volume: 406 start-page: 199 issue: 3–4 year: 2011 ident: 10.1016/j.scitotenv.2018.01.202_bb0350 article-title: Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: an empirical examination publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2011.06.015 – volume: 14 start-page: 209 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0380 article-title: Adaptive neuro-fuzzy computing technique for precipitation estimation – volume: 25 start-page: 891 year: 2010 ident: 10.1016/j.scitotenv.2018.01.202_bb0315 article-title: Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2010.02.003 – volume: 79 start-page: 61 year: 1998 ident: 10.1016/j.scitotenv.2018.01.202_bb0515 article-title: A practical guide to wavelet analysis publication-title: Bull. Am. Meteorol. Soc. doi: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2 – volume: 17 start-page: 1411 year: 2006 ident: 10.1016/j.scitotenv.2018.01.202_bb0295 article-title: A fast and accurate online sequential learning algorithm for feedforward networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2006.880583 – volume: 173 start-page: 463 year: 2007 ident: 10.1016/j.scitotenv.2018.01.202_bb0210 article-title: The likely impact of elevated [CO2], nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: a literature review publication-title: New Phytol. doi: 10.1111/j.1469-8137.2007.01967.x – volume: 171-172 start-page: 1 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0120 article-title: Quantifying biosphere–atmosphere exchange of CO2 using eddy covariance, wavelet denoising, neural networks, and multiple regression models publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2012.11.002 – volume: 28 start-page: 1215 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0465 article-title: Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning publication-title: Hydrol. Process. doi: 10.1002/hyp.9669 – volume: 120 start-page: 1941 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0365 article-title: Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks publication-title: J. Geophys. Res. Biogeosci. doi: 10.1002/2015JG002997 – volume: 119 start-page: 31 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0095 article-title: Influence and predictive capacity of climate anomalies on daily to decadal extremes in canopy photosynthesis publication-title: Photosynth. Res. doi: 10.1007/s11120-013-9925-z – volume: 33 start-page: 772 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0540 article-title: Solar radiation prediction using artificial neural network techniques: a review publication-title: Renew. Sust. Energ. Rev. doi: 10.1016/j.rser.2013.08.055 – volume: 20 start-page: 1394 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0020 article-title: Climate-driven uncertainties in modeling terrestrial gross primary production: a site level to global-scale analysis publication-title: Glob. Chang. Biol. doi: 10.1111/gcb.12474 – volume: 117 start-page: 53 year: 2003 ident: 10.1016/j.scitotenv.2018.01.202_bb0140 article-title: Ecophysiological controls on the carbon balances of three southern boreal forests publication-title: Agric. For. Meteorol. doi: 10.1016/S0168-1923(03)00023-6 – volume: 38 start-page: 6894 year: 2017 ident: 10.1016/j.scitotenv.2018.01.202_bb0175 article-title: A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2017.1368098 – volume: SMC-15 start-page: 116 year: 1985 ident: 10.1016/j.scitotenv.2018.01.202_bb0495 article-title: Fuzzy identification of systems and its applications to modeling and control publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1985.6313399 – volume: 36 start-page: 480 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0005 article-title: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting publication-title: Prog. Phys. Geogr. doi: 10.1177/0309133312444943 – volume: 30 start-page: 1189 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0230 article-title: Adaptation of ANFIS model to assess thermal comfort of an urban square in moderate and dry climate publication-title: Stoch. Env. Res. Risk A. doi: 10.1007/s00477-015-1116-3 – volume: 101 start-page: 568 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0240 article-title: A simulation model for visitors' thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies publication-title: Energy doi: 10.1016/j.energy.2016.02.021 – volume: 25 start-page: 887 year: 2005 ident: 10.1016/j.scitotenv.2018.01.202_bb0480 article-title: Variability in net ecosystem exchange from hourly to inter-annual time scales at adjacent pine and hardwood forests: a wavelet analysis publication-title: Tree Physiol. doi: 10.1093/treephys/25.7.887 – volume: 61 start-page: 585 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0080 article-title: Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models publication-title: Hydrol. Sci. J. doi: 10.1080/02626667.2014.988155 – volume: 34 start-page: 179 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0250 article-title: Prediction of long-term monthly air temperature using geographical inputs publication-title: Int. J. Climatol. doi: 10.1002/joc.3676 – volume: 2 start-page: 27 year: 2011 ident: 10.1016/j.scitotenv.2018.01.202_bb0060 – volume: 116 year: 2011 ident: 10.1016/j.scitotenv.2018.01.202_bb0145 article-title: Seasonal variation of photosynthetic model parameters and leaf area index from global Fluxnet eddy covariance data publication-title: J. Geophys. Res. Biogeosci. doi: 10.1029/2011JG001742 – volume: 124 start-page: 991 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0235 article-title: Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method publication-title: Theor. Appl. Climatol. doi: 10.1007/s00704-015-1462-6 – volume: 404 start-page: 411 year: 2008 ident: 10.1016/j.scitotenv.2018.01.202_bb0160 article-title: The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2007.11.007 – volume: 150 start-page: 1476 year: 2010 ident: 10.1016/j.scitotenv.2018.01.202_bb0550 article-title: Interannual variation of evapotranspiration from forest and grassland ecosystems in western Canada in relation to drought publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2010.08.003 – volume: 12 year: 2017 ident: 10.1016/j.scitotenv.2018.01.202_bb0435 article-title: Land-use dynamics influence estimates of carbon sequestration potential in tropical second-growth forest publication-title: Environ. Res. Lett. doi: 10.1088/1748-9326/aa708b – volume: 59 start-page: 387 year: 2007 ident: 10.1016/j.scitotenv.2018.01.202_bb0150 article-title: A decade of carbon, water and energy flux measurements of an old spruce forest at the Anchor Station Tharandt publication-title: Tellus Ser. B Chem. Phys. Meteorol. doi: 10.1111/j.1600-0889.2007.00259.x – volume: 30 start-page: 641 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0455 article-title: Precipitation estimation using support vector machine with discrete wavelet transform publication-title: Water Resour. Manag. doi: 10.1007/s11269-015-1182-9 – volume: 23 start-page: 273 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0245 article-title: Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle publication-title: Ecol. Appl. doi: 10.1890/12-0747.1 – volume: 36 start-page: 285 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0055 article-title: Self-adaptive evolutionary extreme learning machine publication-title: Neural. Process. Lett. doi: 10.1007/s11063-012-9236-y – volume: 43 start-page: 4576 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0510 article-title: Large–scale variation in boreal and temperate forest carbon turnover rate related to climate publication-title: Geophys. Res. Lett. doi: 10.1002/2016GL068794 – volume: 18 start-page: 566 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0410 article-title: Terrestrial biosphere models need better representation of vegetation phenology: results from the north American carbon program site synthesis publication-title: Glob. Chang. Biol. doi: 10.1111/j.1365-2486.2011.02562.x – volume: 140 start-page: 322 year: 2006 ident: 10.1016/j.scitotenv.2018.01.202_bb0025 article-title: Surface energy balance closure by the eddy-covariance method above three boreal forest stands and implications for the measurement of the CO2 flux publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2006.08.007 – volume: 70 start-page: 489 year: 2006 ident: 10.1016/j.scitotenv.2018.01.202_bb0185 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 68 start-page: 306 year: 2005 ident: 10.1016/j.scitotenv.2018.01.202_bb0290 article-title: Fully complex extreme learning machine publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.03.002 – volume: 150 start-page: 1090 year: 2010 ident: 10.1016/j.scitotenv.2018.01.202_bb0375 article-title: Age effects on carbon fluxes in temperate pine forests publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2010.04.008 – volume: 30 start-page: 5217 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0475 article-title: Prediction of water-level in the Urmia Lake using the extreme learning machine approach publication-title: Water Resour. Manag. doi: 10.1007/s11269-016-1480-x – volume: 10 start-page: 8185 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0090 article-title: Testing the applicability of neural networks as a gap-filling method using CH4 flux data from high latitude wetlands publication-title: Biogeosciences doi: 10.5194/bg-10-8185-2013 – volume: 535 start-page: 457 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0555 article-title: Evaluation of data driven models for river suspended sediment concentration modeling publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.02.012 – volume: 83 start-page: 147 year: 1997 ident: 10.1016/j.scitotenv.2018.01.202_bb0015 article-title: Seasonal variation of carbon dioxide exchange rates above and below a boreal jack pine forest publication-title: Agric. For. Meteorol. doi: 10.1016/S0168-1923(96)02335-0 – volume: 26 start-page: 4347 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0415 article-title: Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques publication-title: Water Resour. Manag. doi: 10.1007/s11269-012-0148-4 – volume: 189 start-page: 305 year: 2005 ident: 10.1016/j.scitotenv.2018.01.202_bb0320 article-title: Artificial neural network application for multi-ecosystem carbon flux simulation publication-title: Ecol. Model. doi: 10.1016/j.ecolmodel.2005.03.014 – volume: 113 year: 2008 ident: 10.1016/j.scitotenv.2018.01.202_bb0275 article-title: Factors controlling the interannual variability in the carbon balance of a southern boreal black spruce forest publication-title: J. Geophys. Res. doi: 10.1029/2007JD008965 – volume: 10 start-page: 187 year: 2007 ident: 10.1016/j.scitotenv.2018.01.202_bb0355 article-title: Age-dependent changes in ecosystem carbon fluxes in managed forests in northern Wisconsin, USA publication-title: Ecosystems doi: 10.1007/s10021-007-9018-y – volume: 21 start-page: 2861 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0130 article-title: Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts publication-title: Glob. Chang. Biol. doi: 10.1111/gcb.12916 – volume: 2 start-page: 107 year: 2011 ident: 10.1016/j.scitotenv.2018.01.202_bb0195 article-title: Extreme learning machines: a survey publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-011-0019-y – volume: 117 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0420 article-title: A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis publication-title: J. Geophys. Res.-Biogeosci. doi: 10.1029/2012JG001960 – volume: 21 start-page: 1737 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0305 article-title: Predictability of the terrestrial carbon cycle publication-title: Glob. Chang. Biol. doi: 10.1111/gcb.12766 – volume: 104 start-page: 68 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0405 article-title: Selection of the most influential flow and thermal parameters for predicting the efficiency of activated carbon filters using neuro-fuzzy technique publication-title: Build. Environ. doi: 10.1016/j.buildenv.2016.04.031 – volume: 14 start-page: 3445 year: 2017 ident: 10.1016/j.scitotenv.2018.01.202_bb0035 article-title: Boreal coniferous forest density leads to significant variations in soil physical and geochemical properties publication-title: Biogeosciences doi: 10.5194/bg-14-3445-2017 – volume: 23 start-page: 665 year: 1993 ident: 10.1016/j.scitotenv.2018.01.202_bb0215 article-title: ANFIS: adaptive-network-based fuzzy inference system publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/21.256541 – volume: 23 start-page: 1240 year: 2017 ident: 10.1016/j.scitotenv.2018.01.202_bb0535 article-title: Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales publication-title: Glob. Chang. Biol. doi: 10.1111/gcb.13509 – volume: 62 start-page: 106 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0225 article-title: Thermal sensation prediction by soft computing methodology publication-title: J. Therm. Biol. doi: 10.1016/j.jtherbio.2016.07.005 – volume: 93 start-page: 406 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0335 article-title: Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2015.01.021 – volume: 246 start-page: 11 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0525 article-title: State-dependent errors in a land surface model across biomes inferred from eddy covariance observations on multiple timescales publication-title: Ecol. Model. doi: 10.1016/j.ecolmodel.2012.07.017 – volume: 13 start-page: 370 year: 2007 ident: 10.1016/j.scitotenv.2018.01.202_bb0430 article-title: A method for deriving net primary productivity and component respiratory fluxes from tower-based eddy covariance data: a case study using a 17-year data record from a Douglas-fir chronosequence publication-title: Glob. Chang. Biol. doi: 10.1111/j.1365-2486.2006.01298.x – volume: 107 start-page: 71 year: 2001 ident: 10.1016/j.scitotenv.2018.01.202_bb0125 article-title: Gap filling strategies for long term energy flux data sets publication-title: Agric. For. Meteorol. doi: 10.1016/S0168-1923(00)00235-5 – volume: 454 start-page: 903 year: 1998 ident: 10.1016/j.scitotenv.2018.01.202_bb0180 article-title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis publication-title: Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. doi: 10.1098/rspa.1998.0193 – volume: 46 start-page: 72 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0470 article-title: Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing publication-title: Hydrol. Res. doi: 10.2166/nh.2013.112 – volume: 233 start-page: 12 year: 2017 ident: 10.1016/j.scitotenv.2018.01.202_bb0220 article-title: Direct and indirect controls of the interannual variability in atmospheric CO2 exchange of three contrasting ecosystems in Denmark publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2016.10.023 – volume: 94 start-page: 727 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0445 article-title: Support vector machine-based exergetic modelling of a DI diesel engine running on biodiesel–diesel blends containing expanded polystyrene publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2015.10.140 – volume: 12 start-page: 2115 year: 2006 ident: 10.1016/j.scitotenv.2018.01.202_bb0485 article-title: Separating the effects of climate and vegetation on evapotranspiration along a successional chronosequence in the southeastern US publication-title: Glob. Chang. Biol. doi: 10.1111/j.1365-2486.2006.01244.x – volume: 6 start-page: 1897 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0110 article-title: Impact of nitrogen fertilization on forest carbon sequestration and water loss in a chronosequence of three Douglas-fir stands in the Pacific Northwest publication-title: Forests doi: 10.3390/f6061897 – volume: 488 start-page: 17 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0500 article-title: Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.02.022 – volume: 118 start-page: 28 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0065 article-title: Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2015.08.020 – volume: 154-155 start-page: 99 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0085 article-title: Quantifying the influence of climate and biological drivers on the interannual variability of carbon exchanges in European forests through process-based modelling publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2011.10.010 – volume: 14 start-page: 133 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0425 article-title: Terrestrial carbon sink observed from space: variation of growth rates and seasonal cycle amplitudes in response to interannual surface temperature variability publication-title: Atmos. Chem. Phys. doi: 10.5194/acp-14-133-2014 – volume: 398 start-page: 292 year: 2011 ident: 10.1016/j.scitotenv.2018.01.202_bb0075 article-title: Evapotranspiration estimation by two different neuro-fuzzy inference systems publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2010.12.030 – volume: 542 start-page: 603 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0545 article-title: Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.09.035 – volume: 66 start-page: 247 year: 2011 ident: 10.1016/j.scitotenv.2018.01.202_bb0345 article-title: Support vector machines in remote sensing: a review publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2010.11.001 – volume: 42 start-page: 491 year: 2011 ident: 10.1016/j.scitotenv.2018.01.202_bb0460 article-title: Estimating daily pan evaporation from climatic data of the state of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) publication-title: Hydrol. Res. doi: 10.2166/nh.2011.020 – volume: 509 start-page: 25 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0300 article-title: Improving real time flood forecasting using fuzzy inference system publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.11.021 – volume: 5 start-page: 969 year: 2008 ident: 10.1016/j.scitotenv.2018.01.202_bb0285 article-title: Modeling carbon dynamics in two adjacent spruce forests with different soil conditions in Russia publication-title: Biogeosciences doi: 10.5194/bg-5-969-2008 – volume: 108 start-page: 293 year: 2001 ident: 10.1016/j.scitotenv.2018.01.202_bb0010 article-title: Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes publication-title: Agric. For. Meteorol. doi: 10.1016/S0168-1923(01)00244-1 – volume: 160 start-page: 249 year: 2003 ident: 10.1016/j.scitotenv.2018.01.202_bb0135 article-title: Review and comparison of methods to study the contribution of variables in artificial neural network models publication-title: Ecol. Model. doi: 10.1016/S0304-3800(02)00257-0 – volume: 351 start-page: 9 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0070 article-title: Influence of different tree-harvesting intensities on forest soil carbon stocks in boreal and northern temperate forest ecosystems publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2015.04.034 – volume: 221 start-page: 575 year: 2010 ident: 10.1016/j.scitotenv.2018.01.202_bb0390 article-title: Identification of important factors for water vapor flux and CO2 exchange in a cropland publication-title: Ecol. Model. doi: 10.1016/j.ecolmodel.2009.11.007 – volume: 40 start-page: 14 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0050 article-title: Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering publication-title: Comput. Geotech. doi: 10.1016/j.compgeo.2011.09.008 – volume: 8 start-page: 498 year: 2017 ident: 10.1016/j.scitotenv.2018.01.202_bb0105 article-title: Modeling and predicting carbon and water fluxes using data-driven techniques in a forest ecosystem publication-title: Forests doi: 10.3390/f8120498 – volume: 6 start-page: 2121 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0205 article-title: The north American carbon program multi-scale synthesis and terrestrial model Intercomparison project-part 1: overview and experimental design publication-title: Geosci. Model Dev. doi: 10.5194/gmd-6-2121-2013 – volume: 31 start-page: 575 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0490 article-title: Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration publication-title: Irrig. Sci. doi: 10.1007/s00271-012-0332-6 – volume: 84 start-page: 2145 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0450 article-title: Design of a support vector machine with different kernel functions to predict scour depth around bridge piers publication-title: Nat. Hazards doi: 10.1007/s11069-016-2540-5 – volume: 33 start-page: 847 year: 2007 ident: 10.1016/j.scitotenv.2018.01.202_bb0190 article-title: Credit scoring with a data mining approach based on support vector machines publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2006.07.007 – volume: 138 start-page: 244 year: 2006 ident: 10.1016/j.scitotenv.2018.01.202_bb0400 article-title: Uncertainties in measurement and modelling of net ecosystem exchange of a forest publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2006.05.007 – volume: 6 start-page: 45 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0280 article-title: Quantifying the model structural error in carbon cycle data assimilation systems publication-title: Geosci. Model Dev. doi: 10.5194/gmd-6-45-2013 – volume: 16 start-page: 1021 year: 2002 ident: 10.1016/j.scitotenv.2018.01.202_bb0040 article-title: Landscapes as patches of plant functional types: an integrating concept for climate and ecosystem models publication-title: Glob. Biogeochem. Cycles doi: 10.1029/2000GB001360 – volume: 25 start-page: 549 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0100 article-title: Extreme learning machine and its applications publication-title: Neural Comput. & Applic. doi: 10.1007/s00521-013-1522-8 – volume: 134 start-page: 109 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0440 article-title: Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: a case study for Iran publication-title: J. Atmos. Sol. Terr. Phys. doi: 10.1016/j.jastp.2015.09.014 – volume: 11 start-page: 217 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0165 article-title: Evaluating terrestrial CO2 flux diagnoses and uncertainties from a simple land surface model and its residuals publication-title: Biogeosciences doi: 10.5194/bg-11-217-2014 – volume: 30 start-page: 642 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0030 article-title: Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.02.011 – volume: 96 start-page: 311 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0340 article-title: Using ANFIS for selection of more relevant parameters to predict dew point temperature publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2015.11.081 – volume: 147 start-page: 209 year: 2007 ident: 10.1016/j.scitotenv.2018.01.202_bb0330 article-title: Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2007.08.011 – volume: 29 start-page: 33 year: 2002 ident: 10.1016/j.scitotenv.2018.01.202_bb0045 article-title: Long-term variations of climate and carbon fluxes over the Amazon basin publication-title: Geophys. Res. Lett. doi: 10.1029/2001GL013607 – volume: 12 start-page: 1205 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0385 article-title: Uncertainty analysis of eddy covariance CO2 flux measurements for different EC tower distances using an extended two-tower approach publication-title: Biogeosciences doi: 10.5194/bg-12-1205-2015 – volume: 19 start-page: 372 year: 2014 ident: 10.1016/j.scitotenv.2018.01.202_bb0395 article-title: Support vector machine applications in the field of hydrology: a review publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.02.002 – volume: 148 start-page: 942 year: 2008 ident: 10.1016/j.scitotenv.2018.01.202_bb0530 article-title: Effects of different eddy covariance correction schemes on energy balance closure and comparisons with the modified Bowen ratio system publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2008.01.005 – volume: 5 start-page: 891 year: 1999 ident: 10.1016/j.scitotenv.2018.01.202_bb0170 article-title: Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest publication-title: Glob. Chang. Biol. doi: 10.1046/j.1365-2486.1999.00281.x – volume: 101 start-page: 312 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0325 article-title: Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2014.11.006 – volume: 10 start-page: 203 year: 2018 ident: 10.1016/j.scitotenv.2018.01.202_bb0115 article-title: Estimating forest carbon fluxes using machine learning techniques based on eddy covariance measurements publication-title: Sustainability doi: 10.3390/su10010203 – volume: 13 start-page: 193 year: 1998 ident: 10.1016/j.scitotenv.2018.01.202_bb0310 article-title: The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study publication-title: Environ. Model. Softw. doi: 10.1016/S1364-8152(98)00020-6 – volume: 51 start-page: 108 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0270 article-title: Modeling rainfall-runoff process using soft computing techniques publication-title: Computer. Geosci. U. K. doi: 10.1016/j.cageo.2012.07.001 – volume: 23 start-page: 3545 year: 2009 ident: 10.1016/j.scitotenv.2018.01.202_bb0370 article-title: Modelling evapotranspiration using discrete wavelet transform and neural networks publication-title: Hydrol. Process. doi: 10.1002/hyp.7448 – volume: 138 start-page: 349 year: 2011 ident: 10.1016/j.scitotenv.2018.01.202_bb0255 article-title: Generalized neurofuzzy models for estimating daily pan evaporation values from weather data publication-title: J. Irrig. Drain. Eng. doi: 10.1061/(ASCE)IR.1943-4774.0000403 – volume: 26 start-page: 457 year: 2012 ident: 10.1016/j.scitotenv.2018.01.202_bb0260 article-title: Intermittent streamflow forecasting by using several data driven techniques publication-title: Water Resour. Manag. doi: 10.1007/s11269-011-9926-7 – volume: 9 start-page: 525 year: 2003 ident: 10.1016/j.scitotenv.2018.01.202_bb0360 article-title: A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization publication-title: Glob. Chang. Biol. doi: 10.1046/j.1365-2486.2003.00609.x – volume: 88 start-page: 66 year: 2016 ident: 10.1016/j.scitotenv.2018.01.202_bb0520 article-title: Artificial neural network application in comparison with modeling allometric equations for predicting above-ground biomass in the Hyrcanian mixed-beech forests of Iran publication-title: Biomass Bioenergy doi: 10.1016/j.biombioe.2016.03.020 – volume: 5 start-page: 1625 year: 2008 ident: 10.1016/j.scitotenv.2018.01.202_bb0505 article-title: Assessing seasonality of biochemical CO2 exchange model parameters from micrometeorological flux observations at boreal coniferous forest publication-title: Biogeosciences doi: 10.5194/bg-5-1625-2008 – volume: 61 start-page: 32 year: 2015 ident: 10.1016/j.scitotenv.2018.01.202_bb0200 article-title: Trends in extreme learning machines: a review publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.10.001 – volume: 22 start-page: 92 year: 2013 ident: 10.1016/j.scitotenv.2018.01.202_bb0265 article-title: Modeling of dissolved oxygen in river water using artificial intelligence techniques publication-title: J. Environ. Inform. doi: 10.3808/jei.201300248 |
| SSID | ssj0000781 |
| Score | 2.4593987 |
| Snippet | With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 78 |
| SubjectTerms | Adaptive neuro-fuzzy inference system carbon Carbon fluxes chronosequences Data-driven techniques eddy covariance Extreme learning machine Flux towers Forest ecosystems forests fuzzy logic neural networks support vector machines viability |
| Title | Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation |
| URI | https://dx.doi.org/10.1016/j.scitotenv.2018.01.202 https://www.ncbi.nlm.nih.gov/pubmed/29426202 https://www.proquest.com/docview/2001068904 https://www.proquest.com/docview/2237519041 |
| Volume | 627 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier) customDbUrl: eissn: 1879-1026 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000781 issn: 0048-9697 databaseCode: GBLVA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier Science Direct Journals customDbUrl: eissn: 1879-1026 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000781 issn: 0048-9697 databaseCode: AIKHN dateStart: 19950106 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Complete Freedom Collection [SCCMFC] customDbUrl: eissn: 1879-1026 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000781 issn: 0048-9697 databaseCode: ACRLP dateStart: 19950106 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection customDbUrl: eissn: 1879-1026 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000781 issn: 0048-9697 databaseCode: .~1 dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals customDbUrl: mediaType: online eissn: 1879-1026 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000781 issn: 0048-9697 databaseCode: AKRWK dateStart: 19930115 isFulltext: true providerName: Library Specific Holdings |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1La9wwEBYhoVAoId02zfYRptCrG0sev3ILYcO2S3MoKc3NWA-HlK0d1t7QXPpL-mM749cSaJtDT8K2ZAlpZjQjzcwnxLtEYuFClJ6O0tgjCik8jUZ6KnAa8yRVpsVY-nQezb_gx8vwckucDrEw7FbZy_5OprfSun9z1M_m0c31Ncf4YpJSD5JTWGHAchgxZhSD9z83bh6czKa7ZSbGptr3fLzov01Fuukt-3glnL9T9ecrf9ih_qaBtjvR2Z7Y7VVIOOlG-VRsuXIiHnWgkncTsT_bxK5RtZ5564l40h3RQRd59Ez8mhF7s8JaXgGprtQlmHylqxKK5fqHq4F94vnTegUDjkoD7FLq2RULSRgTwNbAu6EFarusyiuP5T04a-_AVLdkjTNpwffNcWR9DAzCtgQzwiBCXlrY5B5_Li7OZhenc68Ha_AM-th4UmMRxj5KmbucZtMYJoLCpXERGt-aKFSJy7VFDJUrNOclc2SYk4phbCB1sC-2y6p0BwKcSchoi11IthxadIlvUhtHJvUTjYGWUxEN65OZPpE542kss8Fj7Vs2LmzGC5v5kko1Ff7Y8KbL5fFwk-OBALJ7ZJnRjvNw47cDyWTEtHwTk5euWteM_UmmeJL6-I86KohJvaYZnYoXHb2No1ZpCySgXv7P8F6Jx_zEfm8yfC22m9XavSENq9GHLQsdip2TD4v5OZeLz18XvwE5yixR |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELaqIgQSqmChsC2PQeJqGidO4vRWVVst0Pa0SL1Z8SNV0ZJUm2xFL_wSfiwzea0qAT1wihTbieV5eMaemY-xD0rIwsdScJNkKUcOKbiRVvAw8kbmKgtti7F0dp7Mv8rPF_HFFjsecmEorLLX_Z1Ob7V1_-agX82D66sryvGVKsM_CCphJSPUww9kHKbkgX38uYnzoGo23TUzSjZ2vxPkhR9uKjRObyjIS1EBz7A_YPnDFvU3E7Tdik6esp3ehoSjbprP2JYvJ-xhhyp5O2G7s03yGnbrpbeesCfdGR10qUfP2a8ZyjdZrOUloO2KvwSbr0xVQrFc__A1UFA8Na1XMACpNEAxpdytSEvCWAG2BtoOHeDYZVVeclL44J27BVvdoDtOvAXfN-eR9SEQCtsS7IiDCHnpYFN8_AVbnMwWx3PeozVwKwPZcGFkEaeBFCL3Oa6mtcQFhc_SIraBs0kcKp8bJ5FIvjBUmMyjZ442hnWRMNEu2y6r0r9i4K1Cry31MTpz0kmvApu5NLFZoIyMjJiyZKCPtn0lcwLUWOohZO2bHgmribA6EPgMpywYB153xTzuH3I4MIC-w5cat5z7B78fWEaj1NJVTF76al0T-Cf64ioL5D_6hFGK9jWu6JS97PhtnHWYtUgC4d7_TO8dezRfnJ3q00_nX_bZY2qhIDgRv2bbzWrt36C51Zi3rTj9BoCDLEM |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Estimating+forest+carbon+fluxes+using+four+different+data-driven+techniques+based+on+long-term+eddy+covariance+measurements%3A+Model+comparison+and+evaluation&rft.jtitle=The+Science+of+the+total+environment&rft.au=Dou%2C+Xianming&rft.au=Yang%2C+Yongguo&rft.date=2018-06-15&rft.eissn=1879-1026&rft.volume=627&rft.spage=78&rft_id=info:doi/10.1016%2Fj.scitotenv.2018.01.202&rft_id=info%3Apmid%2F29426202&rft.externalDocID=29426202 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0048-9697&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0048-9697&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0048-9697&client=summon |