A Comparative Study of Machine Learning Algorithms for Flood Risk Prediction

Floods are one of the most frequent and devastating natural disasters, significantly affecting human life, infrastructure, and the environment. Accurate and timely flood prediction is essential for disaster preparedness and mitigation. This study presents an intelligent flood risk prediction framework using machine learning and deep learning techniques applied to four Indian cities—Pune, Nashik, Kolhapur, and Satara—each associated with major rivers such as the Mula-Mutha, Godavari, Panchganga, and Krishna, respectively. The dataset incorporates a comprehensive set of hydrometeorological and environmental features including rainfall, temperature, humidity, wind speed, water level, discharge, groundwater level, soil moisture, atmospheric pressure, evaporation rate, and historical flood events. Four algorithms—Random Forest, Support Vector Machine (SVM), XG-Boost, and Artificial Neural Networks (ANN)—were trained and evaluated to predict the flood_risk level. The model performances were compared using accuracy, precision, recall, F1-score, and ROC-AUC. The results demonstrate that the integration of multiple data sources and ensemble techniques significantly improves predictive performance. This research contributes to the development of smart, data-driven flood early warning systems tailored for regional hydrological conditions.