Optimizing Flood Preparedness: A Comprehensive to Refine Rainfall Predict with Ensemble Machine Learning Models

The main goal of this study was to develop a rainfall model for the Kolhapur District in Maharashtra, India, utilizing ensemble machine learning methods. For this purpose, five separate models - Linear Regression (LR), Random Forest (RF), K-Nearest neighbors (KNN), Support Vector Regression (SVR), and Gradient Boosting Regressor (GBR) - were employed. To improve the model’s effectiveness, a weighted average ensemble (WAE) approach was utilized. Data from a period of fifteen years (2008–2022) was utilized to adjust and confirm the accuracy of the models. Assessment of the models was carried out utilizing root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient efficiency (NSE), and the coefficient of determination (R2). The GBR model showed better predictive accuracy than the rest, with NSE = 0.381, MAE = 5.425, and RMSE = 8.393 in the validation stage. Nonetheless, the weighted average ensemble (WAE) model produced improved outcomes with NSE = 0.409, MAE = 5.306, and RMSE = 8.207. In summary, this research showed the considerable promise of ensemble methods in rainfall forecasting modeling.