Limited flood zoning regulations and lack of flood control response units in developing countries make the flood problems more severe. This study presents a new framework for categorizing the floodplain into critical risk zones by considering hydraulic and topographical aspects related to flood zoning. Framework was developed by integrating output of Mike Hydro River Model with Artificial Neural Network (ANN) technique which was explored in the lower part of Damodar river basin (Jharkhand, India). Total nine flood causing factors were selected in three layers of ANN architecture which were optimized by grid search technique. Confusion matrix was employed to check the unevenness and disproportionality in datasets from which 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 result was presented in GIS environment which shows 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 sensitivity parameters. Further, this study contributes towards flood susceptibility mapping thereby supporting the hydrologists in course of action and decisions for combating the floods in the watersheds.
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