Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

Abstract The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors)...

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Autores principales:	Hamid Reza Pourghasemi, Narges Kariminejad, Mahdis Amiri, Mohsen Edalat, Mehrdad Zarafshar, Thomas Blaschke, Artemio Cerda
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e49a5dfb79724dd68433a0e3aa2570e7
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spelling	oai:doaj.org-article:e49a5dfb79724dd68433a0e3aa2570e72021-12-02T14:22:00ZAssessing and mapping multi-hazard risk susceptibility using a machine learning technique10.1038/s41598-020-60191-32045-2322https://doaj.org/article/e49a5dfb79724dd68433a0e3aa2570e72020-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-60191-3https://doaj.org/toc/2045-2322Abstract The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. For four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.Hamid Reza PourghasemiNarges KariminejadMahdis AmiriMohsen EdalatMehrdad ZarafsharThomas BlaschkeArtemio CerdaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hamid Reza Pourghasemi Narges Kariminejad Mahdis Amiri Mohsen Edalat Mehrdad Zarafshar Thomas Blaschke Artemio Cerda Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
description	Abstract The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. For four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.
format	article
author	Hamid Reza Pourghasemi Narges Kariminejad Mahdis Amiri Mohsen Edalat Mehrdad Zarafshar Thomas Blaschke Artemio Cerda
author_facet	Hamid Reza Pourghasemi Narges Kariminejad Mahdis Amiri Mohsen Edalat Mehrdad Zarafshar Thomas Blaschke Artemio Cerda
author_sort	Hamid Reza Pourghasemi
title	Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
title_short	Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
title_full	Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
title_fullStr	Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
title_full_unstemmed	Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
title_sort	assessing and mapping multi-hazard risk susceptibility using a machine learning technique
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e49a5dfb79724dd68433a0e3aa2570e7
work_keys_str_mv	AT hamidrezapourghasemi assessingandmappingmultihazardrisksusceptibilityusingamachinelearningtechnique AT nargeskariminejad assessingandmappingmultihazardrisksusceptibilityusingamachinelearningtechnique AT mahdisamiri assessingandmappingmultihazardrisksusceptibilityusingamachinelearningtechnique AT mohsenedalat assessingandmappingmultihazardrisksusceptibilityusingamachinelearningtechnique AT mehrdadzarafshar assessingandmappingmultihazardrisksusceptibilityusingamachinelearningtechnique AT thomasblaschke assessingandmappingmultihazardrisksusceptibilityusingamachinelearningtechnique AT artemiocerda assessingandmappingmultihazardrisksusceptibilityusingamachinelearningtechnique
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Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

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