<![CDATA[Rainfall forecasting is essential for water resource management, hydrometeorological disaster mitigation, and agricultural planning. This study addressed the limitations of previous research that focused on single models or hybrid approaches combining only two methods, which often failed to capture the simultaneous volatility, trend, and nonlinear characteristics of rainfall data. Monthly rainfall data from 2015 to 2024 were analyzed using three individual models: Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Holt’s Exponential Smoothing, and Backpropagation Neural Network (BPNN). Two hybrid models, GARCH–Holt and GARCH–Holt–BPNN, were also developed to integrate the advantages of statistical and artificial intelligence methods. Hyperparameter tuning was performed to optimize model performance, and forecasting accuracy was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results showed that GARCH effectively captured short-term volatility, Holt followed trends and seasonal patterns, and BPNN modeled nonlinear relationships despite sensitivity to data variations. The GARCH–Holt hybrid improved stability and accuracy compared to individual models, while the GARCH–Holt–BPNN hybrid achieved the highest predictive performance with a MAPE of 1.13%, indicating strong generalization capability. Forecasted rainfall for 2025 revealed seasonal patterns characterized by periods of heavy, moderate, and light rainfall. A MATLAB-based Graphical User Interface (GUI) was developed to facilitate interactive modeling and visualization. Overall, the proposed hybrid GARCH–Holt–BPNN model provides a more robust and reliable forecasting framework by effectively integrating volatility, trend, and nonlinear components, thereby enhancing predictive accuracy and supporting data-driven decision-making in hydrometeorological applications.]]>
