River flow modeling for flood prediction using machine learning techniques in Godavari river, India

Abstract

Floods are highly impactful natural calamities, inflicting significant damage to infrastructure and causing numerous fatalities. These devastating events occur when rivers exceed their capacity or breach their banks due to intense precipitation.
Forecasting river flow in the minimum Godavari river basin of eastern India allowed researchers to examine the potential of four data-driven techniques, including the artificial neural network (ANN), support vector machine (SVM), nonlinear autoregressive network with exogenous input (NARX) and Gaussian process regression (GPR), and compare the outcomes to those of the proposed neuro-tree method. By combining values of the antecedent river flow from two gauging stations, various models were built utilizing the methodologies, and the results were compared to see which models had the best match. The performances of the generated models were examined using mean square error, coefficient of correlation (R), and Nash-Sutcliffe coefficient, three widely used statistical performance assessment metrics (NS).
An extensive analysis of the overall performance indicators revealed that the proposed neuro-tree algorithm models were more effective in flood prediction than the other four techniques employed in this study.