Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria
Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this stud...
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| Published in | Environmental science and pollution research international Vol. 31; no. 41; pp. 54204 - 54233 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1614-7499 0944-1344 1614-7499 |
| DOI | 10.1007/s11356-023-25291-3 |
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| Abstract | Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models—multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)—were integrated and validated to predict the IWQ parameters. The coefficient of determination (
R
2
) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO
3
, Cl, Mg, and SO
4
were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction. |
|---|---|
| AbstractList | Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R
) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO
, Cl, Mg, and SO
were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction. Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models—multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)—were integrated and validated to predict the IWQ parameters. The coefficient of determination (R²) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO₃, Cl, Mg, and SO₄ were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction. Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models—multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)—were integrated and validated to predict the IWQ parameters. The coefficient of determination (R2) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO3, Cl, Mg, and SO4 were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction. Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R2) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO3, Cl, Mg, and SO4 were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction.Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R2) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO3, Cl, Mg, and SO4 were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction. Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models—multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)—were integrated and validated to predict the IWQ parameters. The coefficient of determination ( R 2 ) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO 3 , Cl, Mg, and SO 4 were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction. |
| Author | Nwodo, Ogechukwu M. Ugar, Samuel I. Anyanwu, Ifeanyi E. Omeka, Michael E. Onwuka, Obialo S. Igwe, Ogbonnaya Undiandeye, Peter A. |
| Author_xml | – sequence: 1 givenname: Michael E. orcidid: 0000-0003-0405-8616 surname: Omeka fullname: Omeka, Michael E. email: omeka.ekuru@unical.edu.ng, michaelekuru@gmail.com organization: Department of Geology, University of Calabar – sequence: 2 givenname: Ogbonnaya surname: Igwe fullname: Igwe, Ogbonnaya organization: Department of Geology, Faculty of Physical Sciences, University of Nigeria – sequence: 3 givenname: Obialo S. surname: Onwuka fullname: Onwuka, Obialo S. organization: Department of Geology, Faculty of Physical Sciences, University of Nigeria – sequence: 4 givenname: Ogechukwu M. surname: Nwodo fullname: Nwodo, Ogechukwu M. organization: Centre for Atmospheric Research, Kogi State University – sequence: 5 givenname: Samuel I. surname: Ugar fullname: Ugar, Samuel I. organization: Department of Geology, University of Calabar – sequence: 6 givenname: Peter A. surname: Undiandeye fullname: Undiandeye, Peter A. organization: Department of Geology, University of Calabar – sequence: 7 givenname: Ifeanyi E. orcidid: 0000-0002-0935-5479 surname: Anyanwu fullname: Anyanwu, Ifeanyi E. organization: Department of Geology, Faculty of Physical Sciences, University of Nigeria |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36723836$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1108_JM2_09_2023_0202 crossref_primary_10_1007_s40808_023_01777_4 crossref_primary_10_1007_s12665_025_12173_9 crossref_primary_10_1007_s40808_024_02123_y crossref_primary_10_1007_s11356_024_32552_2 crossref_primary_10_1007_s10653_023_01737_y crossref_primary_10_1007_s11356_025_36120_0 crossref_primary_10_3390_agriculture13081545 crossref_primary_10_1016_j_gsd_2025_101405 crossref_primary_10_1016_j_eja_2024_127441 crossref_primary_10_3389_fenvs_2023_1116220 crossref_primary_10_1007_s10661_023_11923_1 crossref_primary_10_1007_s41748_023_00357_x crossref_primary_10_3390_info14110583 crossref_primary_10_2166_ws_2024_242 crossref_primary_10_1007_s12517_024_12008_0 |
| Cites_doi | 10.1016/j.marpolbul.2021.112907 10.1007/s00521-016-2404-7 10.1016/j.gexplo.2018.07.003 10.1007/s12517-022-10514-7 10.1016/0377-2217(90)90057-I 10.1080/03067319.2021.1907359 10.1007/s10661-008-0633-7 10.1007/s12665-013-2757-5 10.1007/s10653-018-0194-9 10.1007/s40808-019-00581-3 10.1007/s10661-022-09856-2 10.1016/0301-9268(87)90004-0 10.1016/j.jssas.2020.08.001 10.1007/s11269-008-9321-1 10.1016/j.jafrearsci.2014.01.004 10.1007/s11356-022-18520-8 10.1016/j.landusepol.2012.04.023 10.1016/0899-5362(88)90061-9 10.1080/19942060.2020.1861987 10.1007/s13201-014-0154-1 10.1007/s11269-014-0817-6 10.1007/978-1-4615-1665-1 10.1016/S0043-1354(00)00036-1 10.1080/19475705.2017.1407368 10.1016/j.gsd.2021.100545 10.1016/0034-4257(94)90020-5 10.1007/s12665-014-3972-4 10.1016/J.ECOLMODEL.2010.09.007 10.1016/0043-1354(71)90097-2 10.1016/j.jksus.2019.01.005 10.1007/s13201-022-01590-x 10.1007/s40808-016-0250-3 10.4236/jwarp.2019.116046 10.1080/03067319.2021.1873316 10.1016/j.jenvman.2008.11.008 10.1023/B:EMAS.0000031715.83752.a1 10.1007/s10064-013-0538-8 10.1080/19942060.2021.1984992 10.1007/s12665-015-4905-6 10.1007/s10661-020-08832-y 10.1016/j.hydres.2021.04.001 10.11591/ijai.v9.i1.pp126-134 10.30492/ijcce.2020.39800 10.1007/s11269-013-0364-6 10.1097/00010694-196512000-00001 10.1007/s11356-020-10156-w 10.1080/19475705.2015.1045043 10.1002/hyp.10879 10.1007/s12517-020-05813-w 10.1007/s11269-017-1685-7 10.1080/00031305.2000.10474502 10.1007/s11356-018-3751-y 10.3390/w12020471 10.1007/s00477-014-0899-y 10.1097/00010694-196306000-00003 10.1007/s11069-020-04141-2 10.1007/s40808-020-01041-z 10.1007/s13201-021-01556-5 10.1016/j.chemosphere.2018.11.193 10.20944/preprints202003.0088.v1 10.2136/sssaj1961.03615995002500010016x 10.5067/ASTER/ASTGTM.003 10.1007/s10653-022-01332-7 10.1016/j.jhydrol.2020.124974 10.1016/j.jics.2022.100479 10.1007/s40808-022-01502-7 10.1007/978-3-031-04707-7_8 10.1007/978-3-031-04707-7_9 10.1007/s42108-022-00183-3 10.1007/s42108-021-00128-2 10.1016/j.geogeo.2022.100104 |
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| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
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| DOI | 10.1007/s11356-023-25291-3 |
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| Issue | 41 |
| Keywords | Irrigation water quality Analytic hierarchy process (AHP) Artificial neural networks (ANNs) Machine learning Lower Benue Trough |
| Language | English |
| License | 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
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| References | Omeka, Egbueri, Unigwe (CR65) 2022; 15 CR38 Wagh, Panaskar, Muley, Mukate, Lolage, Aamalawar (CR94) 2016; 2 Beven (CR14) 2016; 30 Kouadri, Samir (CR47) 2021; 40 Wang (CR96) 2009; 23 Richards (CR79) 1954; 60 Hussain, Joshi, Singhal, Kumar, Rao (CR35) 2012; 4 Moran, Clarke, Inoue, Vidal (CR58) 1994; 49 Nhantumbo, Carvalho, Uvo, Larsson, Larson (CR63) 2018; 193 Ray, Kumar, Kumar, Rai, Khandelwal, Singh (CR78) 2020 Benjmel, Amraoui, Boutaleb, Ouchchen, Tahiri, Touab (CR13) 2020; 12 Aravinthasamy, Karunanidhi, Subramani, Anand, Roy, Srinivasamoorthy (CR8) 2019; 12 Mohammed, Scholz (CR59) 2017; 31 Barzegar, Moghaddam, Adamowski, Nazemi (CR11) 2018; 87 Cay, Uyan (CR17) 2013; 30 CR43 Prati, Pavanello, Pesarin (CR74) 1971; 5 CR42 Ozel, Gemici, Gemici, Ozel, Cetin, Sevik (CR68) 2020 Aykut (CR10) 2021; 12 Bouaroudj, Menad, Bounamous (CR15) 2019; 219 Bozdağ (CR16) 2015; 73 Ifediegwu (CR36) 2022; 12 Chowdary, Chakraborthy, Jeyaram, Murthy, Sharma, Dadhwal (CR18) 2013; 27 Wang, Liu, Zhang, Chen (CR95) 2011; 222 Omeka, Igwe, Unigwe (CR66) 2022; 194 Mokhtar, Elbeltagi, Gyasi-Agyei, Al-Ansari, Abdel-Fattah (CR57) 2022; 12 CR55 CR54 Shunmugapriya, Panneerselvam, Muniraj, Ravichandran, Prasath, Thomas, Duraisamy (CR85) 2021; 172 Greaves (CR32) 2011 CR51 Kaur, Rishi, Sharma, Sharma, Lata, Singh (CR45) 2019; 31 Kaur, Rishi, Arora (CR44) 2021; 193 Adamu, Nganje, Edet (CR3) 2015; 3 Kelley (CR46) 1963; 95 Singh, Madhuri, Arora (CR86) 2019; 1 Mohammadpour, Shaharuddin, Zakaria, Ghani, Vakili, Chan (CR56) 2016; 75 Song, Kim (CR87) 2009; 90 Pesce, Wunderlin (CR73) 2000; 34 CR67 CR64 Gaya, Abba, Abdu, Tukur, Saleh, Esmaili, Wahab (CR30) 2020; 9 CR61 CR60 Ighalo, Adeniyi, Marques (CR37) 2020 Saaty, Vargas (CR82) 1994 Panneerselvam, Paramasivam, Karuppannan, Ravichandran, Selvaraj (CR71) 2020; 13 Egbueri, Unigwe, Omeka (CR25) 2021 Kalantar, Pradhan, Naghibi, Motevalli, Mansor (CR41) 2018; 9 Kudamnya, Sylvanus, Essien, Vulegbo, Omang (CR48) 2019; 11 Saaty (CR83) 1990; 48 Menard (CR53) 2000; 54 Freeze, Cherry (CR29) 1979 Pan, Ng, Fallah, Richter (CR69) 2019; 26 Lloyd, Heathcote (CR52) 1985 Horton, Hawkins (CR34) 1965; 100 Ghavidel, Montazeri (CR31) 2014; 28 CR2 Saaty, Vargas (CR81) 2001 CR4 CR6 CR7 CR9 Papaioannou, Vasiliades, Loukas (CR72) 2014 CR89 CR84 CR80 Egbueri, Agbasi (CR24) 2022; 29 El Bilali, Taleb (CR27) 2020 Ragunath (CR75) 1987 Panneerselvam, Muniraj, Pande, Ravichandran (CR70) 2021 Edet, Okereke (CR22) 2014; 92 Tao, Al-Khafaji, Qi, Kermani, Kisi, Tiyasha, Chau, Nourani, Melesse, Elhakeem, Farooque, Nejadhashemi, Khedher, Alawi, Deo, Shahid, Singh, Yaseen (CR90) 2021; 15 Hameed, Sharqi, Yaseen, Afan, Hussain, Elshafe (CR33) 2017; 28 CR12 Sun, Rajabtabar, Samadi, Rezaie-Balf, Ghaemi, Band, Mosavi (CR88) 2021; 15 Juahir, Zain, Toriman (CR39) 2004; 16 CR97 Todd, Mays (CR91) 2005 Rahmati, Zeinivand, Besharat (CR77) 2015 Nagaraju, Sunil Kumar, Thejaswi (CR62) 2014; 4 CR93 CR92 Adamu, Nganje, Edet (CR1) 2014; 71 Aju, Achu, Raicy, Raghunath (CR5) 2021; 4 Kadam, Wagh, Muley, Umrikar, Sankhua (CR40) 2019 Rahman, Ekwere, Azmatullah, Ukpong (CR76) 1988; 7 Kumar, Rammohan, Sahayam, Jeevanandam (CR49) 2009; 159 CR28 CR23 CR21 CR20 Liou, Lo, Wang (CR50) 2004; 96 Corominas, van Westen, Frattini, Cascini, Malet, Fotopoulou, Catani, Van Den Eeckhaut, Mavrouli, Agliardi, Pitilakis, Winter, Pastor, Ferlisi, Tofani, Hervas, Smith (CR19) 2014; 73 Ekwueme (CR26) 1987; 34 H Juahir (25291_CR39) 2004; 16 25291_CR9 J Corominas (25291_CR19) 2014; 73 25291_CR7 ME Omeka (25291_CR65) 2022; 15 25291_CR38 RA Freeze (25291_CR29) 1979 25291_CR6 SZZ Ghavidel (25291_CR31) 2014; 28 25291_CR4 25291_CR2 W Wang (25291_CR95) 2011; 222 MS Gaya (25291_CR30) 2020; 9 TL Saaty (25291_CR83) 1990; 48 S Bouaroudj (25291_CR15) 2019; 219 B Kalantar (25291_CR41) 2018; 9 25291_CR23 25291_CR28 L Kaur (25291_CR45) 2019; 31 25291_CR20 25291_CR21 HM Ragunath (25291_CR75) 1987 A Ray (25291_CR78) 2020 HM Hussain (25291_CR35) 2012; 4 JH Horton (25291_CR34) 1965; 100 25291_CR51 MS Moran (25291_CR58) 1994; 49 25291_CR54 25291_CR55 A Nagaraju (25291_CR62) 2014; 4 R Mohammadpour (25291_CR56) 2016; 75 L Prati (25291_CR74) 1971; 5 ME Omeka (25291_CR66) 2022; 194 G Singh (25291_CR86) 2019; 1 K Shunmugapriya (25291_CR85) 2021; 172 SK Kumar (25291_CR49) 2009; 159 R Mohammed (25291_CR59) 2017; 31 DK Todd (25291_CR91) 2005 25291_CR42 25291_CR43 A Edet (25291_CR22) 2014; 92 T Song (25291_CR87) 2009; 90 T Cay (25291_CR17) 2013; 30 O Rahmati (25291_CR77) 2015 A Bozdağ (25291_CR16) 2015; 73 25291_CR80 CI Adamu (25291_CR1) 2014; 71 C Nhantumbo (25291_CR63) 2018; 193 TL Saaty (25291_CR82) 1994 L Kaur (25291_CR44) 2021; 193 S Liou (25291_CR50) 2004; 96 G Papaioannou (25291_CR72) 2014 P Aravinthasamy (25291_CR8) 2019; 12 K Benjmel (25291_CR13) 2020; 12 VM Chowdary (25291_CR18) 2013; 27 A Mokhtar (25291_CR57) 2022; 12 VM Wagh (25291_CR94) 2016; 2 M Hameed (25291_CR33) 2017; 28 AK Kadam (25291_CR40) 2019 25291_CR67 CD Aju (25291_CR5) 2021; 4 25291_CR60 25291_CR61 W Kelley (25291_CR46) 1963; 95 C Pan (25291_CR69) 2019; 26 25291_CR64 T Saaty (25291_CR81) 2001 LA Richards (25291_CR79) 1954; 60 S Kouadri (25291_CR47) 2021; 40 S Menard (25291_CR53) 2000; 54 SI Ifediegwu (25291_CR36) 2022; 12 EA Kudamnya (25291_CR48) 2019; 11 CI Adamu (25291_CR3) 2015; 3 JO Ighalo (25291_CR37) 2020 25291_CR12 SF Pesce (25291_CR73) 2000; 34 H Tao (25291_CR90) 2021; 15 R Barzegar (25291_CR11) 2018; 87 B Panneerselvam (25291_CR70) 2021 HU Ozel (25291_CR68) 2020 25291_CR92 25291_CR93 BN Ekwueme (25291_CR26) 1987; 34 AMS Rahman (25291_CR76) 1988; 7 K Sun (25291_CR88) 2021; 15 25291_CR97 X Wang (25291_CR96) 2009; 23 T Aykut (25291_CR10) 2021; 12 JC Egbueri (25291_CR25) 2021 JW Lloyd (25291_CR52) 1985 A El Bilali (25291_CR27) 2020 25291_CR89 K Beven (25291_CR14) 2016; 30 GE Greaves (25291_CR32) 2011 JC Egbueri (25291_CR24) 2022; 29 B Panneerselvam (25291_CR71) 2020; 13 25291_CR84 |
| References_xml | – ident: CR97 – year: 1979 ident: CR29 publication-title: Groundwater – volume: 16 start-page: 42 year: 2004 end-page: 55 ident: CR39 article-title: Application of artificial neural network models for predicting water quality index publication-title: Malaysian J Civ Eng – volume: 172 year: 2021 ident: CR85 article-title: Integration of multi-criteria decision analysis and GIS for evaluating the site suitability for aquaculture in southern coastal region, India publication-title: Marine Pollut Bull doi: 10.1016/j.marpolbul.2021.112907 – volume: 28 start-page: 893 issue: 1 year: 2017 end-page: 905 ident: CR33 article-title: Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region Malaysia publication-title: Neural Comput Applic doi: 10.1007/s00521-016-2404-7 – ident: CR51 – volume: 193 start-page: 32 year: 2018 end-page: 40 ident: CR63 article-title: Applicability of a processes-based model and artificial neural networks to estimate the concentration of major ions in rivers publication-title: J Geochem Exploration doi: 10.1016/j.gexplo.2018.07.003 – volume: 15 start-page: 1 issue: 13 year: 2022 end-page: 24 ident: CR65 article-title: Investigating the hydrogeochemistry, corrosivity, and scaling tendencies of groundwater in an agrarian area (Nigeria) using graphical, indexical, and statistical modeling publication-title: Arab J Geosci doi: 10.1007/s12517-022-10514-7 – volume: 48 start-page: 9 issue: 1 year: 1990 end-page: 26 ident: CR83 article-title: How to make a decision: the analytic hierarchy process publication-title: Eur J Oper Res doi: 10.1016/0377-2217(90)90057-I – year: 2021 ident: CR25 article-title: Ayejoto DA (2021) Urban groundwater quality assessment using pollution indicators and multivariate statistical tools: a case study in southeast Nigeria publication-title: Int J Environ Anal Chem doi: 10.1080/03067319.2021.1907359 – volume: 159 start-page: 341 year: 2009 end-page: 351 ident: CR49 article-title: Assessment of groundwater quality and hydrogeochemistry of Manimuktha River basin, Tamil Nadu, India publication-title: Environ Monit Asses doi: 10.1007/s10661-008-0633-7 – ident: CR54 – ident: CR80 – volume: 71 start-page: 3793 year: 2014 end-page: 3811 ident: CR1 article-title: Hydrochemical assessment of pond and stream water near abandoned barite mine sites in parts of Oban massif and Mamfe Embayment, Southeastern Nigeria publication-title: Environ Earth Sci doi: 10.1007/s12665-013-2757-5 – ident: CR42 – volume: 87 start-page: 141 year: 2018 end-page: 653 ident: CR11 article-title: Assessing the potential origins and human health risks of trace elements in groundwater: a case study in the Khoy plain Iran publication-title: Environ Geochem Health doi: 10.1007/s10653-018-0194-9 – year: 2019 ident: CR40 article-title: Prediction of water quality index using artificial neural network and multiple linear regression modeling approaches in Shivganga River basin India publication-title: Model Earth Syst Environ doi: 10.1007/s40808-019-00581-3 – ident: CR92 – volume: 1 start-page: 1438 year: 2019 ident: CR86 article-title: Integrated GIS-based modeling approach for irrigation water quality suitability zonation in parts of Satluj River Basin, Bist Doab region, North India publication-title: Appl Sci – start-page: 294 year: 1985 ident: CR52 publication-title: Natural inorganic hydrochemistry in relation to groundwater – volume: 194 start-page: 1 issue: 3 year: 2022 end-page: 30 ident: CR66 article-title: An integrated approach to the bioavailability, ecological, and health risk assessment of potentially toxic elements in soils within a barite mining area, SE Nigeria publication-title: Environ Monit Asses doi: 10.1007/s10661-022-09856-2 – year: 2011 ident: CR32 publication-title: On water augmentation strategies for small island developing states: a case study of Bequia – ident: CR60 – volume: 34 start-page: 269 issue: 3–4 year: 1987 end-page: 289 ident: CR26 article-title: Structural orientations and Precambrian deformational episodes of Uwet area Oban massif, SE Nigeria publication-title: Precam Res doi: 10.1016/0301-9268(87)90004-0 – year: 2020 ident: CR27 article-title: Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment publication-title: J Saudi Soc Agric Sci doi: 10.1016/j.jssas.2020.08.001 – volume: 23 start-page: 1171 year: 2009 end-page: 1182 ident: CR96 article-title: A proposal and application of the integrated benefit assessment model for urban water resources exploitation and utilization publication-title: Water Resour Manage doi: 10.1007/s11269-008-9321-1 – volume: 92 start-page: 25 year: 2014 end-page: 44 ident: CR22 article-title: Hydrogeologic framework of the shallow aquifers in the Ikom-Mamfe Embayment, Nigeria using an integrated approach publication-title: J African Earth Sci doi: 10.1016/j.jafrearsci.2014.01.004 – ident: CR89 – volume: 29 start-page: 38346 issue: 25 year: 2022 end-page: 38373 ident: CR24 article-title: Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-022-18520-8 – volume: 30 start-page: 541 year: 2013 end-page: 548 ident: CR17 article-title: Evaluation of reallocation criteria in land consolidation studies using the analytic hierarchy process (AHP) publication-title: Land Use Policy doi: 10.1016/j.landusepol.2012.04.023 – ident: CR6 – volume: 7 start-page: 149 issue: 1 year: 1988 end-page: 157 ident: CR76 article-title: Petrology and geochemistry of granitic intrusive rocks from the western part of the Oban Massif, southeastern Nigeria publication-title: J African Earth Sci (and the Middle East) doi: 10.1016/0899-5362(88)90061-9 – volume: 15 start-page: 251 issue: 1 year: 2021 end-page: 271 ident: CR88 article-title: An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction publication-title: Engin Appl Comput Fluid Mech doi: 10.1080/19942060.2020.1861987 – volume: 4 start-page: 385 year: 2014 end-page: 396 ident: CR62 article-title: Assessment of groundwater quality for irrigation: a case study from Bandalamottu lead mining area, Guntur District, Andhra Pradesh, South India publication-title: Appl Water Sci doi: 10.1007/s13201-014-0154-1 – ident: CR38 – year: 2014 ident: CR72 article-title: Multi-criteria analysis framework for potential flood prone areas mapping publication-title: Water Resour Manage doi: 10.1007/s11269-014-0817-6 – year: 2001 ident: CR81 publication-title: Methods, concepts and applications of the analytic hierarchy process doi: 10.1007/978-1-4615-1665-1 – ident: CR55 – volume: 34 start-page: 2915 issue: 11 year: 2000 end-page: 2926 ident: CR73 article-title: Use of water quality indices to verify the impact of Cordoba City (Argentina) on Suquıa river publication-title: Water Res doi: 10.1016/S0043-1354(00)00036-1 – volume: 9 start-page: 49 year: 2018 end-page: 69 ident: CR41 article-title: Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) publication-title: Geomat Nat Hazard Risk doi: 10.1080/19475705.2017.1407368 – volume: 12 year: 2021 ident: CR10 article-title: Determination of groundwater potential zones using geographical information systems (GIS) and analytic hierarchy process (AHP) between Edirne-Kalkansogut (northwestern Turkey) publication-title: Groundw Sustain Dev doi: 10.1016/j.gsd.2021.100545 – volume: 12 start-page: 55 year: 2019 end-page: 87 ident: CR8 article-title: Fluoride contamination in groundwater of the Shanmuganadhi River Basin (south India) and its association with other chemical constituents using geographical information system and multivariate statistics publication-title: Geochem – volume: 49 start-page: 246 issue: 3 year: 1994 end-page: 263 ident: CR58 article-title: Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index publication-title: Remote Sens Environ doi: 10.1016/0034-4257(94)90020-5 – ident: CR93 – volume: 73 start-page: 8217 issue: 12 year: 2015 end-page: 8236 ident: CR16 article-title: Combining AHP with GIS for assessment of irrigation water quality in Çumra irrigation district (Konya), Central Anatolia, Turkey publication-title: Environ Earth Sci doi: 10.1007/s12665-014-3972-4 – ident: CR4 – ident: CR12 – volume: 222 start-page: 307 year: 2011 end-page: 312 ident: CR95 article-title: Assessment of a model of pollution disaster in near-shore coastal waters based on catastrophe theory publication-title: Ecol Modell doi: 10.1016/J.ECOLMODEL.2010.09.007 – ident: CR61 – volume: 5 start-page: 741 year: 1971 end-page: 751 ident: CR74 article-title: Assessment of surface water quality by a single index of pollution publication-title: Water Res doi: 10.1016/0043-1354(71)90097-2 – ident: CR84 – volume: 31 start-page: 1245 issue: 4 year: 2019 end-page: 1253 ident: CR45 article-title: Hydrogeochemical characterization of groundwater in alluvial plains of River Yamuna in Northern India: an insight of controlling processes publication-title: J King Saud University-Science doi: 10.1016/j.jksus.2019.01.005 – volume: 12 start-page: 1 issue: 4 year: 2022 end-page: 14 ident: CR57 article-title: Prediction of irrigation water quality indices based on machine learning and regression models publication-title: Appl Water Sci doi: 10.1007/s13201-022-01590-x – ident: CR21 – volume: 2 start-page: 1 issue: 4 year: 2016 end-page: 10 ident: CR94 article-title: Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra India publication-title: Model Earth Syst Environ doi: 10.1007/s40808-016-0250-3 – volume: 60 start-page: 210 year: 1954 end-page: 220 ident: CR79 article-title: Diagnosis and improvement of saline and alkali soils publication-title: Agriculture Handbook – volume: 11 start-page: 758 issue: 06 year: 2019 ident: CR48 article-title: A 2D GIS Approach for Mapping Aquiferous Zones Using Remotely Sensed Data within Obubra, Southeast-Nigeria publication-title: J Water Resour Prot doi: 10.4236/jwarp.2019.116046 – year: 2021 ident: CR70 publication-title: Prediction and evaluation of groundwater characteristics using the radial basic model in Semi-arid region doi: 10.1080/03067319.2021.1873316 – volume: 90 start-page: 1534 year: 2009 end-page: 1543 ident: CR87 article-title: Development of a water quality loading index based on water quality modeling publication-title: J Environ Manage doi: 10.1016/j.jenvman.2008.11.008 – volume: 96 start-page: 35 year: 2004 end-page: 52 ident: CR50 article-title: A generalized water quality index for Taiwan publication-title: Environ Monitor Assess doi: 10.1023/B:EMAS.0000031715.83752.a1 – ident: CR67 – volume: 73 start-page: 209 year: 2014 end-page: 263 ident: CR19 article-title: Recommendations for the quantitative analysis of landslide risk publication-title: Bull Eng Geol Environ doi: 10.1007/s10064-013-0538-8 – start-page: 563 year: 1987 ident: CR75 publication-title: Groundwater – volume: 15 start-page: 1585 issue: 1 year: 2021 end-page: 1612 ident: CR90 article-title: Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions publication-title: Engin Appl Comput Fluid Mech doi: 10.1080/19942060.2021.1984992 – volume: 75 start-page: 1 year: 2016 end-page: 12 ident: CR56 article-title: Prediction of water quality index in free surface constructed wetlands publication-title: Environ Earth Sci doi: 10.1007/s12665-015-4905-6 – ident: CR9 – volume: 193 start-page: 1 issue: 3 year: 2021 end-page: 22 ident: CR44 article-title: Deciphering pollution vulnerability zones of River Yamuna in relation to existing land use land cover in Panipat, Haryana, India publication-title: Environ Monit Assess doi: 10.1007/s10661-020-08832-y – volume: 4 start-page: 24 year: 2021 end-page: 37 ident: CR5 article-title: Identification of suitable sites and structures for artificial groundwater recharge for sustainable water resources management in Vamanapuram River Basin, South India publication-title: HydroResearch doi: 10.1016/j.hydres.2021.04.001 – ident: CR64 – volume: 9 start-page: 126 issue: 1 year: 2020 end-page: 134 ident: CR30 article-title: Estimation of water quality index using artificial intelligence approaches and multi-linear regression publication-title: IAES Int J Artif Intell doi: 10.11591/ijai.v9.i1.pp126-134 – volume: 40 start-page: 1315 issue: 4 year: 2021 end-page: 1333 ident: CR47 article-title: Hydro-chemical study with geospatial analysis of groundwater Quality Illizi Region, South-East of Algeria publication-title: Iran J Chem Chemical Eng (IJCCE) doi: 10.30492/ijcce.2020.39800 – ident: CR43 – volume: 27 start-page: 3555 year: 2013 end-page: 3571 ident: CR18 article-title: Multi-criteria decision-making approach for watershed prioritization using analytic hierarchy process technique and GIS publication-title: Water Resour Manage doi: 10.1007/s11269-013-0364-6 – volume: 100 start-page: 377 issue: 6 year: 1965 end-page: 383 ident: CR34 article-title: Flow path of rain from the soil surface to the water table publication-title: Soil Sci doi: 10.1097/00010694-196512000-00001 – ident: CR2 – volume: 3 start-page: 10 year: 2015 end-page: 21 ident: CR3 article-title: Heavy metal contamination and health risk assessment associated with abandoned barite mines in Cross River State, southeastern Nigeria publication-title: Environ Nanotech, Monit, Managt – year: 2020 ident: CR68 article-title: Application of artificial neural networks to predict the heavy metal contamination in the Bartin River publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-10156-w – year: 2015 ident: CR77 article-title: Flood hazard zoning in Yasooj region, Iran, using GIS and multicriteria decision analysis publication-title: Geomatics, Natural Hazards Risk doi: 10.1080/19475705.2015.1045043 – start-page: 636 year: 2005 ident: CR91 publication-title: Groundwater hydrology – volume: 30 start-page: 3578 issue: 20 year: 2016 end-page: 3582 ident: CR14 article-title: Advice to a young hydrologist publication-title: Hydrol Process doi: 10.1002/hyp.10879 – volume: 4 start-page: 44 year: 2012 end-page: 50 ident: CR35 article-title: Development of an index of aquifer water quality within the GIS environment publication-title: Iran J Earth Sci – volume: 13 start-page: 1 issue: 17 year: 2020 end-page: 22 ident: CR71 article-title: A GIS-based evaluation of hydrochemical characterization of groundwater in hard rock region, South Tamil Nadu, India publication-title: Arab J Geosci doi: 10.1007/s12517-020-05813-w – ident: CR23 – volume: 31 start-page: 3557 year: 2017 end-page: 3573 ident: CR59 article-title: Adaptation strategy to mitigate the impact of climate change on water resources in arid and semi-arid regions: a case study publication-title: Water Resour Manag doi: 10.1007/s11269-017-1685-7 – volume: 54 start-page: 17 issue: 1 year: 2000 end-page: 24 ident: CR53 article-title: Coefficients of determination for multiple logistic regression analysis publication-title: Amer Statist doi: 10.1080/00031305.2000.10474502 – volume: 26 start-page: 1821 issue: 2 year: 2019 end-page: 1833 ident: CR69 article-title: Evaluation of the bias and precision of regression techniques and machine learning approaches in total dissolved solids modeling of an urban aquifer publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-018-3751-y – volume: 12 start-page: 471 year: 2020 ident: CR13 article-title: Mapping of groundwater potential zones in crystalline terrain using remote sensing, GIS techniques, and multicriteria data analysis (case of the Ighrem region, Western Anti-Atlas, Morocco) publication-title: Wat doi: 10.3390/w12020471 – volume: 28 start-page: 2101 issue: 8 year: 2014 end-page: 2118 ident: CR31 article-title: Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin publication-title: Stochastic Environ Res Risk Ass doi: 10.1007/s00477-014-0899-y – volume: 95 start-page: 385 year: 1963 end-page: 391 ident: CR46 article-title: Use of saline irrigation water publication-title: Soil Sci doi: 10.1097/00010694-196306000-00003 – year: 2020 ident: CR78 article-title: Stability prediction of Himalayan residual soil slope using artificial neural network publication-title: Nat Hazards doi: 10.1007/s11069-020-04141-2 – year: 2020 ident: CR37 article-title: Artificial intelligence for surface water quality monitoring and assessment A systematic literature analysis publication-title: Model Earth Syst Environ doi: 10.1007/s40808-020-01041-z – ident: CR7 – volume: 12 start-page: 10 year: 2022 ident: CR36 article-title: Assessment of groundwater potential zones using GIS and AHP techniques: a case study of the Lafa district, Nasarawa State, Nigeria publication-title: Appl Water Sci doi: 10.1007/s13201-021-01556-5 – ident: CR28 – year: 1994 ident: CR82 publication-title: Decision making with the Analytic Hierarchy Process – ident: CR20 – volume: 219 start-page: 76 year: 2019 end-page: 88 ident: CR15 article-title: Assessment of water quality at the largest dam in Algeria (Beni Haroun Dam) and effects of irrigation on soil characteristics of agricultural lands publication-title: Chemosphere doi: 10.1016/j.chemosphere.2018.11.193 – volume: 27 start-page: 3555 year: 2013 ident: 25291_CR18 publication-title: Water Resour Manage doi: 10.1007/s11269-013-0364-6 – ident: 25291_CR61 doi: 10.20944/preprints202003.0088.v1 – volume: 15 start-page: 1 issue: 13 year: 2022 ident: 25291_CR65 publication-title: Arab J Geosci doi: 10.1007/s12517-022-10514-7 – volume: 73 start-page: 209 year: 2014 ident: 25291_CR19 publication-title: Bull Eng Geol Environ doi: 10.1007/s10064-013-0538-8 – ident: 25291_CR80 – volume: 13 start-page: 1 issue: 17 year: 2020 ident: 25291_CR71 publication-title: Arab J Geosci doi: 10.1007/s12517-020-05813-w – volume: 4 start-page: 385 year: 2014 ident: 25291_CR62 publication-title: Appl Water Sci doi: 10.1007/s13201-014-0154-1 – volume: 4 start-page: 44 year: 2012 ident: 25291_CR35 publication-title: Iran J Earth Sci – ident: 25291_CR92 doi: 10.2136/sssaj1961.03615995002500010016x – volume: 12 year: 2021 ident: 25291_CR10 publication-title: Groundw Sustain Dev doi: 10.1016/j.gsd.2021.100545 – year: 2021 ident: 25291_CR25 publication-title: Int J Environ Anal Chem doi: 10.1080/03067319.2021.1907359 – volume: 28 start-page: 2101 issue: 8 year: 2014 ident: 25291_CR31 publication-title: Stochastic Environ Res Risk Ass doi: 10.1007/s00477-014-0899-y – volume: 49 start-page: 246 issue: 3 year: 1994 ident: 25291_CR58 publication-title: Remote Sens Environ doi: 10.1016/0034-4257(94)90020-5 – volume-title: Methods, concepts and applications of the analytic hierarchy process year: 2001 ident: 25291_CR81 doi: 10.1007/978-1-4615-1665-1 – volume-title: Decision making with the Analytic Hierarchy Process year: 1994 ident: 25291_CR82 – ident: 25291_CR60 doi: 10.5067/ASTER/ASTGTM.003 – volume: 30 start-page: 541 year: 2013 ident: 25291_CR17 publication-title: Land Use Policy doi: 10.1016/j.landusepol.2012.04.023 – volume: 12 start-page: 1 issue: 4 year: 2022 ident: 25291_CR57 publication-title: Appl Water Sci doi: 10.1007/s13201-022-01590-x – volume: 4 start-page: 24 year: 2021 ident: 25291_CR5 publication-title: HydroResearch doi: 10.1016/j.hydres.2021.04.001 – ident: 25291_CR51 – volume: 12 start-page: 55 year: 2019 ident: 25291_CR8 publication-title: Geochem – volume: 222 start-page: 307 year: 2011 ident: 25291_CR95 publication-title: Ecol Modell doi: 10.1016/J.ECOLMODEL.2010.09.007 – ident: 25291_CR97 – ident: 25291_CR67 doi: 10.1007/s10653-022-01332-7 – year: 2014 ident: 25291_CR72 publication-title: Water Resour Manage doi: 10.1007/s11269-014-0817-6 – volume: 16 start-page: 42 year: 2004 ident: 25291_CR39 publication-title: Malaysian J Civ Eng – volume: 60 start-page: 210 year: 1954 ident: 25291_CR79 publication-title: Agriculture Handbook – year: 2019 ident: 25291_CR40 publication-title: Model Earth Syst Environ doi: 10.1007/s40808-019-00581-3 – volume: 34 start-page: 269 issue: 3–4 year: 1987 ident: 25291_CR26 publication-title: Precam Res doi: 10.1016/0301-9268(87)90004-0 – volume: 31 start-page: 3557 year: 2017 ident: 25291_CR59 publication-title: Water Resour Manag doi: 10.1007/s11269-017-1685-7 – volume: 54 start-page: 17 issue: 1 year: 2000 ident: 25291_CR53 publication-title: Amer Statist doi: 10.1080/00031305.2000.10474502 – volume-title: Groundwater year: 1979 ident: 25291_CR29 – volume: 5 start-page: 741 year: 1971 ident: 25291_CR74 publication-title: Water Res doi: 10.1016/0043-1354(71)90097-2 – year: 2015 ident: 25291_CR77 publication-title: Geomatics, Natural Hazards Risk doi: 10.1080/19475705.2015.1045043 – volume: 100 start-page: 377 issue: 6 year: 1965 ident: 25291_CR34 publication-title: Soil Sci doi: 10.1097/00010694-196512000-00001 – volume: 92 start-page: 25 year: 2014 ident: 25291_CR22 publication-title: J African Earth Sci doi: 10.1016/j.jafrearsci.2014.01.004 – volume: 11 start-page: 758 issue: 06 year: 2019 ident: 25291_CR48 publication-title: J Water Resour Prot doi: 10.4236/jwarp.2019.116046 – volume: 219 start-page: 76 year: 2019 ident: 25291_CR15 publication-title: Chemosphere doi: 10.1016/j.chemosphere.2018.11.193 – year: 2020 ident: 25291_CR78 publication-title: Nat Hazards doi: 10.1007/s11069-020-04141-2 – volume: 87 start-page: 141 year: 2018 ident: 25291_CR11 publication-title: Environ Geochem Health doi: 10.1007/s10653-018-0194-9 – volume: 12 start-page: 10 year: 2022 ident: 25291_CR36 publication-title: Appl Water Sci doi: 10.1007/s13201-021-01556-5 – volume-title: Prediction and evaluation of groundwater characteristics using the radial basic model in Semi-arid region year: 2021 ident: 25291_CR70 doi: 10.1080/03067319.2021.1873316 – ident: 25291_CR89 – year: 2020 ident: 25291_CR68 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-020-10156-w – year: 2020 ident: 25291_CR37 publication-title: Model Earth Syst Environ doi: 10.1007/s40808-020-01041-z – volume: 26 start-page: 1821 issue: 2 year: 2019 ident: 25291_CR69 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-018-3751-y – ident: 25291_CR2 doi: 10.1016/j.jhydrol.2020.124974 – volume: 28 start-page: 893 issue: 1 year: 2017 ident: 25291_CR33 publication-title: Neural Comput Applic doi: 10.1007/s00521-016-2404-7 – volume: 7 start-page: 149 issue: 1 year: 1988 ident: 25291_CR76 publication-title: J African Earth Sci (and the Middle East) doi: 10.1016/0899-5362(88)90061-9 – start-page: 563 volume-title: Groundwater year: 1987 ident: 25291_CR75 – volume: 159 start-page: 341 year: 2009 ident: 25291_CR49 publication-title: Environ Monit Asses doi: 10.1007/s10661-008-0633-7 – start-page: 294 volume-title: Natural inorganic hydrochemistry in relation to groundwater year: 1985 ident: 25291_CR52 – ident: 25291_CR9 – volume: 9 start-page: 126 issue: 1 year: 2020 ident: 25291_CR30 publication-title: IAES Int J Artif Intell doi: 10.11591/ijai.v9.i1.pp126-134 – start-page: 636 volume-title: Groundwater hydrology year: 2005 ident: 25291_CR91 – volume: 23 start-page: 1171 year: 2009 ident: 25291_CR96 publication-title: Water Resour Manage doi: 10.1007/s11269-008-9321-1 – volume: 29 start-page: 38346 issue: 25 year: 2022 ident: 25291_CR24 publication-title: Environ Sci Pollut Res doi: 10.1007/s11356-022-18520-8 – ident: 25291_CR84 – ident: 25291_CR93 doi: 10.1016/j.jics.2022.100479 – volume: 40 start-page: 1315 issue: 4 year: 2021 ident: 25291_CR47 publication-title: Iran J Chem Chemical Eng (IJCCE) doi: 10.30492/ijcce.2020.39800 – ident: 25291_CR28 doi: 10.1007/s40808-022-01502-7 – ident: 25291_CR43 doi: 10.1007/978-3-031-04707-7_8 – volume: 15 start-page: 1585 issue: 1 year: 2021 ident: 25291_CR90 publication-title: Engin Appl Comput Fluid Mech doi: 10.1080/19942060.2021.1984992 – volume: 172 year: 2021 ident: 25291_CR85 publication-title: Marine Pollut Bull doi: 10.1016/j.marpolbul.2021.112907 – volume: 1 start-page: 1438 year: 2019 ident: 25291_CR86 publication-title: Appl Sci – volume: 34 start-page: 2915 issue: 11 year: 2000 ident: 25291_CR73 publication-title: Water Res doi: 10.1016/S0043-1354(00)00036-1 – ident: 25291_CR42 doi: 10.1007/978-3-031-04707-7_9 – ident: 25291_CR6 – volume: 15 start-page: 251 issue: 1 year: 2021 ident: 25291_CR88 publication-title: Engin Appl Comput Fluid Mech doi: 10.1080/19942060.2020.1861987 – volume: 96 start-page: 35 year: 2004 ident: 25291_CR50 publication-title: Environ Monitor Assess doi: 10.1023/B:EMAS.0000031715.83752.a1 – volume: 75 start-page: 1 year: 2016 ident: 25291_CR56 publication-title: Environ Earth Sci doi: 10.1007/s12665-015-4905-6 – ident: 25291_CR20 – volume: 30 start-page: 3578 issue: 20 year: 2016 ident: 25291_CR14 publication-title: Hydrol Process doi: 10.1002/hyp.10879 – ident: 25291_CR55 – ident: 25291_CR7 – volume: 48 start-page: 9 issue: 1 year: 1990 ident: 25291_CR83 publication-title: Eur J Oper Res doi: 10.1016/0377-2217(90)90057-I – volume: 90 start-page: 1534 year: 2009 ident: 25291_CR87 publication-title: J Environ Manage doi: 10.1016/j.jenvman.2008.11.008 – volume: 95 start-page: 385 year: 1963 ident: 25291_CR46 publication-title: Soil Sci doi: 10.1097/00010694-196306000-00003 – volume: 12 start-page: 471 year: 2020 ident: 25291_CR13 publication-title: Wat doi: 10.3390/w12020471 – volume: 71 start-page: 3793 year: 2014 ident: 25291_CR1 publication-title: Environ Earth Sci doi: 10.1007/s12665-013-2757-5 – ident: 25291_CR4 – ident: 25291_CR21 – volume: 2 start-page: 1 issue: 4 year: 2016 ident: 25291_CR94 publication-title: Model Earth Syst Environ doi: 10.1007/s40808-016-0250-3 – ident: 25291_CR64 doi: 10.1007/s42108-022-00183-3 – ident: 25291_CR54 – volume: 3 start-page: 10 year: 2015 ident: 25291_CR3 publication-title: Environ Nanotech, Monit, Managt – ident: 25291_CR38 doi: 10.1007/s42108-021-00128-2 – ident: 25291_CR12 – ident: 25291_CR23 doi: 10.1016/j.geogeo.2022.100104 – volume: 73 start-page: 8217 issue: 12 year: 2015 ident: 25291_CR16 publication-title: Environ Earth Sci doi: 10.1007/s12665-014-3972-4 – volume: 193 start-page: 32 year: 2018 ident: 25291_CR63 publication-title: J Geochem Exploration doi: 10.1016/j.gexplo.2018.07.003 – volume: 9 start-page: 49 year: 2018 ident: 25291_CR41 publication-title: Geomat Nat Hazard Risk doi: 10.1080/19475705.2017.1407368 – volume: 193 start-page: 1 issue: 3 year: 2021 ident: 25291_CR44 publication-title: Environ Monit Assess doi: 10.1007/s10661-020-08832-y – year: 2020 ident: 25291_CR27 publication-title: J Saudi Soc Agric Sci doi: 10.1016/j.jssas.2020.08.001 – volume: 194 start-page: 1 issue: 3 year: 2022 ident: 25291_CR66 publication-title: Environ Monit Asses doi: 10.1007/s10661-022-09856-2 – volume: 31 start-page: 1245 issue: 4 year: 2019 ident: 25291_CR45 publication-title: J King Saud University-Science doi: 10.1016/j.jksus.2019.01.005 – volume-title: On water augmentation strategies for small island developing states: a case study of Bequia year: 2011 ident: 25291_CR32 |
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