Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values
This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with k -NN algorithm to classify and predict tsunami-aff...
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| Published in | PeerJ. Computer science Vol. 8; p. e935 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
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28.03.2022
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| Online Access | Get full text |
| ISSN | 2376-5992 2376-5992 |
| DOI | 10.7717/peerj-cs.935 |
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| Abstract | This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with
k
-NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (
i.e
., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%. |
|---|---|
| AbstractList | This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with k-NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (i.e., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%. This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with -NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices ( ., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%. This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with k -NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices ( i.e ., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%. This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with k-NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (i.e., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%.This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with k-NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (i.e., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%. |
| ArticleNumber | e935 |
| Audience | Academic |
| Author | Hasibuan, Zainal Arifin Hartomo, Kristoko Dwi Nataliani, Yessica |
| Author_xml | – sequence: 1 givenname: Kristoko Dwi orcidid: 0000-0003-0237-851X surname: Hartomo fullname: Hartomo, Kristoko Dwi organization: Department of Information System, Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Indonesia – sequence: 2 givenname: Yessica surname: Nataliani fullname: Nataliani, Yessica organization: Department of Information System, Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Indonesia – sequence: 3 givenname: Zainal Arifin surname: Hasibuan fullname: Hasibuan, Zainal Arifin organization: Faculty of Computer Science, University of Dian Nuswantoro, Semarang, Indonesia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35494821$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.11591/eei.v9i3.1916 10.5539/mas.v4n5p162 10.1016/j.agrformet.2009.03.004 10.1109/LGRS.2014.2314617 10.1155/2017/1353691 10.1175/2009JCLI2900.1 10.1080/01431160600589179 10.1016/j.isprsjprs.2019.04.015 10.1117/12.411533 10.4028/www.scientific.net/KEM.467-469.19 10.1080/01431160304987 10.1515/itms-2015-0009 10.3390/s18082580 10.1007/s11069-015-1595-z 10.1016/S0034-4257(96)00067-3 10.1109/TGRS.2007.895835 10.30536/j.ijreses.2017.v14.a2820 10.1016/j.jag.2011.10.013 10.1016/j.isprsjprs.2020.04.007 10.3390/geosciences10050177 10.1080/01431161.2018.1433343 10.1080/01431160903124682 10.1109/TGRS.2009.2038274 10.1016/0034-4257(88)90106-X 10.3923/ajar.2016.144.153 10.5721/EuJRS20144726 10.1016/j.ejrs.2020.08.003 10.3390/rs10050802 10.47253/jtrss.v4i2.615 10.1155/2017/6824051 10.17014/ijog.v5i3.103 |
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| Copyright | 2022 Hartomo et al. COPYRIGHT 2022 PeerJ. Ltd. 2022 Hartomo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Hartomo et al. 2022 Hartomo et al. |
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| Keywords | Tsunami Risk values Risk areas Vegetation index Spatial |
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| SubjectTerms | Algorithms Algorithms and Analysis of Algorithms Atmospheric correction Atmospheric models Classification Climate change Comparative analysis Data Mining and Machine Learning Disasters Earthquakes Machine learning Methods Neural networks Prediction models Remote sensing Risk Risk areas Risk values Spatial Spatial and Geographic Information Systems Support vector machines Tsunami Tsunamis Vegetation Vegetation index |
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| Title | Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values |
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