ANN and GRNN-Based Coupled Model for Flood Inundation Mapping of the Punpun River Basin

Abstract

The Punpun River is primarily a rain-fed river. Forecasting rainfall accurately would enable an early evaluation of drought and flooding conditions. Therefore, having a flawless model for predicting rainfall is important for the hydrological analysis of any river basin. In this study, Artificial Neural Network (ANN)-based models were developed to predict rainfall and discharge in the basin. During the rainy season, water is spread in and around the area of the watershed, thus a General Regression Neural Network (GRNN)-based model was proposed for fast estimation of the inundation area during the flood taking as input cross-section, rainfall, and discharge. The proposed ANN-GRNN coupled model is the first of its kind for this study area. The assessment of the results shows that the proposed GRNN-based model is capable of estimating the water-spreading area.