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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-affe...

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Autores principales: Hartomo, Kristoko Dwi, Nataliani, Yessica, Hasibuan, Zainal Arifin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044244/
https://www.ncbi.nlm.nih.gov/pubmed/35494821
http://dx.doi.org/10.7717/peerj-cs.935
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author Hartomo, Kristoko Dwi
Nataliani, Yessica
Hasibuan, Zainal Arifin
author_facet Hartomo, Kristoko Dwi
Nataliani, Yessica
Hasibuan, Zainal Arifin
author_sort Hartomo, Kristoko Dwi
collection PubMed
description 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%.
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spelling pubmed-90442442022-04-28 Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values Hartomo, Kristoko Dwi Nataliani, Yessica Hasibuan, Zainal Arifin PeerJ Comput Sci Algorithms and Analysis of Algorithms 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%. PeerJ Inc. 2022-03-28 /pmc/articles/PMC9044244/ /pubmed/35494821 http://dx.doi.org/10.7717/peerj-cs.935 Text en © 2022 Hartomo et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , 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.
spellingShingle Algorithms and Analysis of Algorithms
Hartomo, Kristoko Dwi
Nataliani, Yessica
Hasibuan, Zainal Arifin
Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values
title Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values
title_full Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values
title_fullStr Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values
title_full_unstemmed Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values
title_short Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values
title_sort vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044244/
https://www.ncbi.nlm.nih.gov/pubmed/35494821
http://dx.doi.org/10.7717/peerj-cs.935
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AT natalianiyessica vegetationindicesspatialpredictionbasednovelalgorithmfordeterminingtsunamiriskareasandriskvalues
AT hasibuanzainalarifin vegetationindicesspatialpredictionbasednovelalgorithmfordeterminingtsunamiriskareasandriskvalues