Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN

Limited flood zoning regulations and lack of flood control response units in developing countries make flood problems more severe. This study presents a new framework for categorizing a floodplain into critical risk zones by considering hydraulic and topographical aspects related to flood zoning. Th...

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Autores principales:	Ravindra Kumar Singh, Ashish Soni, Satish Kumar, Srinivas Pasupuleti, Vasanta Govind
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ann data driven model flood zoning hydrodynamic model machine learning Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/463b4882d69645b7a6581614ccfebbed
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spelling	oai:doaj.org-article:463b4882d69645b7a6581614ccfebbed2021-11-06T07:05:19ZZonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN1606-97491607-079810.2166/ws.2020.252https://doaj.org/article/463b4882d69645b7a6581614ccfebbed2021-02-01T00:00:00Zhttp://ws.iwaponline.com/content/21/1/80https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Limited flood zoning regulations and lack of flood control response units in developing countries make flood problems more severe. This study presents a new framework for categorizing a floodplain into critical risk zones by considering hydraulic and topographical aspects related to flood zoning. The framework was developed by integrating output of the MIKE Hydro River Model with an artificial neural network (ANN) technique which was explored in the lower part of Damodar river basin (Jharkhand, India). A total of nine flood causing factors were selected in three layers of ANN architecture which were optimized by a grid search technique. A confusion matrix was employed to check the unevenness and disproportionality in datasets from which were calculated F1 score values for low (0.815), moderate (0.731), high (0.818) and critical (0.64) zones with best overall accuracy of 75.06%. The results were presented in a GIS environment which shows the model correctly predicted 16, 38, 54 and 24 sites under critical, high risk, moderate risk and low risk zones respectively. Elevation and distance from the river were the most sensitive parameters. Further, this study contributes towards flood susceptibility mapping thereby supporting hydrologists in the course of action and decisions for combating floods in watersheds.Ravindra Kumar SinghAshish SoniSatish KumarSrinivas PasupuletiVasanta GovindIWA Publishingarticleanndata driven modelflood zoninghydrodynamic modelmachine learningWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 1, Pp 80-97 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ann data driven model flood zoning hydrodynamic model machine learning Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	ann data driven model flood zoning hydrodynamic model machine learning Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Ravindra Kumar Singh Ashish Soni Satish Kumar Srinivas Pasupuleti Vasanta Govind Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN
description	Limited flood zoning regulations and lack of flood control response units in developing countries make flood problems more severe. This study presents a new framework for categorizing a floodplain into critical risk zones by considering hydraulic and topographical aspects related to flood zoning. The framework was developed by integrating output of the MIKE Hydro River Model with an artificial neural network (ANN) technique which was explored in the lower part of Damodar river basin (Jharkhand, India). A total of nine flood causing factors were selected in three layers of ANN architecture which were optimized by a grid search technique. A confusion matrix was employed to check the unevenness and disproportionality in datasets from which were calculated F1 score values for low (0.815), moderate (0.731), high (0.818) and critical (0.64) zones with best overall accuracy of 75.06%. The results were presented in a GIS environment which shows the model correctly predicted 16, 38, 54 and 24 sites under critical, high risk, moderate risk and low risk zones respectively. Elevation and distance from the river were the most sensitive parameters. Further, this study contributes towards flood susceptibility mapping thereby supporting hydrologists in the course of action and decisions for combating floods in watersheds.
format	article
author	Ravindra Kumar Singh Ashish Soni Satish Kumar Srinivas Pasupuleti Vasanta Govind
author_facet	Ravindra Kumar Singh Ashish Soni Satish Kumar Srinivas Pasupuleti Vasanta Govind
author_sort	Ravindra Kumar Singh
title	Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN
title_short	Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN
title_full	Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN
title_fullStr	Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN
title_full_unstemmed	Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN
title_sort	zonation of flood prone areas by an integrated framework of a hydrodynamic model and ann
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/463b4882d69645b7a6581614ccfebbed
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Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN

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