FLOOD FORECASTING USING MACHINE LEARNING

Abstract

A flood is the inundation of a significant volume of water that exceeds the capacity of a land area, resulting in overflow. The flood prediction (FF) system issues warnings based on the water level or discharge rates via hydraulic infrastructure. Flood forecasting (FF) enhances the capacity and progress in hydrology to reduce risks by using machine learning techniques, namely artificial neural networks (ANN). The use of machine learning algorithms (MLAs) in flood forecasting aims to enhance the system's capacity to predict and reduce flood risks in response to climate change. This study is being conducted to anticipate floods in the Upper Wardha project within the Wardha river basin. Flood forecasting (FF) involves the use of real-time estimate to determine the likelihood of a flood occurring. By using the predicted inflow, the pace at which water enters a reservoir, the timing of operations such as opening and shutting gates may be determined in real-time using artificial neural networks (ANN).