Flood Prediction using Machine Learning

Due to urbanization and climate change, flooding has increased in frequency and severity, upsetting lives and seriously damaging property. Flood Susceptibility Modeling (FSM), which employs sophisticated machine learning approaches, helps identify flood-prone locations and the elements that contribute to these risks in order to solve this problem. This study explores hybrid FSM models that integrate the Index of Entropy (IOE) with Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) to offer a dependable approach for flood prediction and prevention. To assess the predictive power and correlations between influencing elements, the study started with feature selection and multicollinearity analysis. The relationship between several flood-causing components and their total effect on flooding was measured by IOE. Weighted inputs from these findings were used to train the hybrid models. Metrics like the Area Under the Curve (AUC) and other statistical indicators were used to evaluate the models in order to ensure correctness and reliability. The standalone DT model performed the worst (77.0%), while the hybrid DT-IOE model had the best prediction accuracy (87.1%), followed by SVM-IOE and RF-IOE. These findings show that prediction accuracy is increased when machine learning and statistical techniques are combined. 21% of the study region is extremely sensitive to floods, according to the final susceptibility maps, underscoring the major impact of human-induced factors including land-use changes and urban growth. By enhancing feature analysis and prediction accuracy, generative AI significantly enhanced model performance. The significance of hybrid machine learning approaches in developing efficient flood risk management plans is highlighted by this study, which also supports disaster resilience and sustainable urban design.