Revolutionizing flood forecasting with machine learning techniques

Flooding is a major natural disaster that disrupts lives, damages infrastructure, and devastates agriculture, emphasizing the importance of accurate forecasting for better water resource management and disaster preparedness. This research examines flood forecasting techniques at the Ramakona gauging station on the Wainganga River, leveraging 31 years (1987-2017) of peak flow data. The study evaluates two widely used methods: ARIMA and ANN, which are machine-learning approaches. Analysis of historical data revealed significant fluctuations in peak discharge, highlighting the complexity of flood behavior at the site. Results showed that ANN significantly outperformed ARIMA in predicting flood magnitudes, with lower error rates and greater accuracy in capturing nonlinear patterns. These findings demonstrate the potential of ANN as a powerful tool for short-term flood prediction, offering actionable insights for enhancing flood management strategies. The study underscores the role of advanced machine-learning techniques in reducing the impacts of floods and improving water management systems.