<![CDATA[Rainfall forecasting is a fundamental aspect of water resource management, hydrometeorological disaster mitigation, and agricultural planning, all of which are strongly influenced by climate variability. The complexity of rainfall data, characterized by non-linear, non-stationary, and highly fluctuating patterns, necessitates the use of adaptive and accurate predictive approaches. This study aims to conduct a comparative analysis of five forecasting models, namely the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, Holt’s Exponential Smoothing, Backpropagation Neural Network (BPNN), the hybrid GARCH–Holt model, and the advanced hybrid GARCH–Holt–BPNN model, in order to identify the most effective method for monthly rainfall forecasting. Rainfall data for the period 2015–2024 were used for model training and testing. Model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). In addition, this study incorporates the development of a MATLAB-based Graphical User Interface (GUI) to facilitate interactive model implementation and visualization of forecasting results. The results indicate that the GARCH model excels in capturing data volatility, Holt’s Exponential Smoothing effectively follows short-term trends with stability, and BPNN is capable of modeling non-linear relationships despite its sensitivity to data variability. The hybrid GARCH–Holt model demonstrates improved accuracy compared to single models. Furthermore, the hybrid GARCH–Holt–BPNN model achieves the most optimal performance, with an accuracy approaching 99% and the lowest MAPE value of 1.13%, reflecting excellent generalization capability. These findings confirm that the integration of linear and non-linear methods within a hybrid framework significantly enhances rainfall forecasting accuracy and contributes to data-driven decision-making in the field of hydrometeorology.]]>
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