Development of a Flood Forecasting System using Artificial Neural Networks

Floods pose significant risks to communities, infrastructure, and ecosystems worldwide, making early warning systems essential for mitigating their impacts. Traditional flood forecasting models rely on complex mathematical equations and meteorological data, but they often fail to capture the nonlinear relationships between various factors influencing floods. This paper proposes the development of a flood forecasting system using Artificial Neural Networks (ANNs), which can model these nonlinear relationships more effectively. The proposed system uses real-time data from weather stations, river gauges, and historical flood records to predict flood occurrences. The accuracy of the ANN-based system is compared to conventional methods, and it is found to provide superior results in terms of prediction accuracy and reliability. This paper outlines the methodology, benefits, and implementation of the proposed flood forecasting system and discusses its potential for improving flood management and early warning strategies.