Evaluation of multi-hazard map produced using MaxEnt machine learning technique

Abstract Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policym...

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Autores principales:	Narges Javidan, Ataollah Kavian, Hamid Reza Pourghasemi, Christian Conoscenti, Zeinab Jafarian, Jesús Rodrigo-Comino
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f755625f07cb4112a94ad5bd9b123e36
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spelling	oai:doaj.org-article:f755625f07cb4112a94ad5bd9b123e362021-12-02T17:04:06ZEvaluation of multi-hazard map produced using MaxEnt machine learning technique10.1038/s41598-021-85862-72045-2322https://doaj.org/article/f755625f07cb4112a94ad5bd9b123e362021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85862-7https://doaj.org/toc/2045-2322Abstract Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.Narges JavidanAtaollah KavianHamid Reza PourghasemiChristian ConoscentiZeinab JafarianJesús Rodrigo-CominoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Narges Javidan Ataollah Kavian Hamid Reza Pourghasemi Christian Conoscenti Zeinab Jafarian Jesús Rodrigo-Comino Evaluation of multi-hazard map produced using MaxEnt machine learning technique
description	Abstract Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.
format	article
author	Narges Javidan Ataollah Kavian Hamid Reza Pourghasemi Christian Conoscenti Zeinab Jafarian Jesús Rodrigo-Comino
author_facet	Narges Javidan Ataollah Kavian Hamid Reza Pourghasemi Christian Conoscenti Zeinab Jafarian Jesús Rodrigo-Comino
author_sort	Narges Javidan
title	Evaluation of multi-hazard map produced using MaxEnt machine learning technique
title_short	Evaluation of multi-hazard map produced using MaxEnt machine learning technique
title_full	Evaluation of multi-hazard map produced using MaxEnt machine learning technique
title_fullStr	Evaluation of multi-hazard map produced using MaxEnt machine learning technique
title_full_unstemmed	Evaluation of multi-hazard map produced using MaxEnt machine learning technique
title_sort	evaluation of multi-hazard map produced using maxent machine learning technique
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f755625f07cb4112a94ad5bd9b123e36
work_keys_str_mv	AT nargesjavidan evaluationofmultihazardmapproducedusingmaxentmachinelearningtechnique AT ataollahkavian evaluationofmultihazardmapproducedusingmaxentmachinelearningtechnique AT hamidrezapourghasemi evaluationofmultihazardmapproducedusingmaxentmachinelearningtechnique AT christianconoscenti evaluationofmultihazardmapproducedusingmaxentmachinelearningtechnique AT zeinabjafarian evaluationofmultihazardmapproducedusingmaxentmachinelearningtechnique AT jesusrodrigocomino evaluationofmultihazardmapproducedusingmaxentmachinelearningtechnique
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Evaluation of multi-hazard map produced using MaxEnt machine learning technique

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