Development of novel hybridized models for urban flood susceptibility mapping

Abstract Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models b...

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Autores principales:	Omid Rahmati, Hamid Darabi, Mahdi Panahi, Zahra Kalantari, Seyed Amir Naghibi, Carla Sofia Santos Ferreira, Aiding Kornejady, Zahra Karimidastenaei, Farnoush Mohammadi, Stefanos Stefanidis, Dieu Tien Bui, Ali Torabi Haghighi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ad4c0b7a2e9e465e8f97eae8cb6b5e08
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spelling	oai:doaj.org-article:ad4c0b7a2e9e465e8f97eae8cb6b5e082021-12-02T16:06:39ZDevelopment of novel hybridized models for urban flood susceptibility mapping10.1038/s41598-020-69703-72045-2322https://doaj.org/article/ad4c0b7a2e9e465e8f97eae8cb6b5e082020-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-69703-7https://doaj.org/toc/2045-2322Abstract Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.Omid RahmatiHamid DarabiMahdi PanahiZahra KalantariSeyed Amir NaghibiCarla Sofia Santos FerreiraAiding KornejadyZahra KarimidastenaeiFarnoush MohammadiStefanos StefanidisDieu Tien BuiAli Torabi HaghighiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-19 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Omid Rahmati Hamid Darabi Mahdi Panahi Zahra Kalantari Seyed Amir Naghibi Carla Sofia Santos Ferreira Aiding Kornejady Zahra Karimidastenaei Farnoush Mohammadi Stefanos Stefanidis Dieu Tien Bui Ali Torabi Haghighi Development of novel hybridized models for urban flood susceptibility mapping
description	Abstract Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.
format	article
author	Omid Rahmati Hamid Darabi Mahdi Panahi Zahra Kalantari Seyed Amir Naghibi Carla Sofia Santos Ferreira Aiding Kornejady Zahra Karimidastenaei Farnoush Mohammadi Stefanos Stefanidis Dieu Tien Bui Ali Torabi Haghighi
author_facet	Omid Rahmati Hamid Darabi Mahdi Panahi Zahra Kalantari Seyed Amir Naghibi Carla Sofia Santos Ferreira Aiding Kornejady Zahra Karimidastenaei Farnoush Mohammadi Stefanos Stefanidis Dieu Tien Bui Ali Torabi Haghighi
author_sort	Omid Rahmati
title	Development of novel hybridized models for urban flood susceptibility mapping
title_short	Development of novel hybridized models for urban flood susceptibility mapping
title_full	Development of novel hybridized models for urban flood susceptibility mapping
title_fullStr	Development of novel hybridized models for urban flood susceptibility mapping
title_full_unstemmed	Development of novel hybridized models for urban flood susceptibility mapping
title_sort	development of novel hybridized models for urban flood susceptibility mapping
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ad4c0b7a2e9e465e8f97eae8cb6b5e08
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_version_	1718384924239593472

Development of novel hybridized models for urban flood susceptibility mapping

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