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A Comparative Study of Hybrid GARCH–HOLT–BPNN Models for Rainfall Forecasting Using a MATLAB-Based Intelligent Computing System
Corresponding Author(s) : Syaharuddin Syaharuddin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 3, August 2026
Abstract
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.
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References
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M. R. Sheikh and P. Coulibaly, “Review of Recent Developments in Hydrologic Forecast Merging Techniques,” Water, vol. 16, no. 2, p. 301, 2024, https://doi.org/10.3390/w16020301.
M. El Hafyani, K. El Himdi, and S. E. El Adlouni, “Improving monthly precipitation prediction accuracy using machine learning models: A multi-view stacking learning technique,” Front. Water, vol. 6, p. 1378598, 2024, https://doi.org/10.3389/frwa.2024.137859.
E. G. Dada, H. J. Yakubu, and D. O. Oyewola, “Artificial Neural Network Models for Rainfall Prediction,” Eur. J. Electr. Eng. Comput. Sci., vol. 5, no. 2, pp. 30–35, 2021, https://doi.org/10.24018/ejece.2021.5.2.313.
S. Nugroho, “Hydrometeorological disaster risk analysis in Southeast Asia,” Nat. Hazards, vol. 117, pp. 1121–1139, 2023, https://doi.org/10.1007/s11069-023-05851-7.
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H. Simatupang, “Short-term rainfall prediction using Holt’s method,” J. Sains Atmos., vol. 45, no. 2, pp. 110–121, 2023, https://doi.org/10.31227/jsa.v45i2.9988.
L. Parviz, K. Rasouli, and A. T. Haghighi, “Improving hybrid models for precipitation forecasting by combining nonlinear machine learning methods,” Water Resour. Manag., vol. 37, no. 10, pp. 3833–3855, 2023, https://doi.org/10.1007/s11269-023-03528-7.
Z.-C. Zhang, X.-M. Zeng, G. Li, B. Lu, M.-Z. Xiao, and B.-Z. Wang, “Summer precipitation forecast using an optimized artificial neural network with a genetic algorithm for Yangtze–Huaihe River Basin, China,” Atmosphere (Basel)., vol. 13, no. 6, p. 929, 2022, https://doi.org/10.3390/atmos13060929.
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A. N. Aizansi, K. O. Ogunjobi, and F. K. Ogou, “Monthly rainfall prediction using artificial neural network: Case study of the Republic of Benin,” Environ. Data Sci., vol. 3, pp. e10–e10, 2024, https://doi.org/10.1017/eds.2024.10.
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R. Aprianto, A. Tawaqqal, and P. A. D. Puspitasari, “Prediksi Curah Hujan Menggunakan Metode Holt‑Winters di Kabupaten Sumbawa,” Titian Ilmu J. Ilm. Multi Sci., vol. 17, no. 1, pp. 42–52, 2025, https://doi.org/10.30599/eybf7238.
M. A. F. I. Aslim, Jasruddin, P. Palloan, Helmi, M. Arsyad, and H. Triwibowo, “Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency,” Sci. J. Informatics, vol. 10, no. 1, pp. 13–24, 2023, https://doi.org/10.15294/sji.v10i1.37982.
B. A. Rizaldi, A. A. B. Perwita, J. Widjayanto, and M. A. Madjid, “Deep learning of backpropagation neural network algorithm for long‑term predicting rainfall in the Kapuas Hulu, West Kalimantan province of Indonesia,” J. Appl. Nat. Sci., vol. 17, no. 1, pp. 389–397, 2025, https://doi.org/10.31018/jans.v17i1.6183.
D. Karthika and K. Karthikeyan, “Performance of combined forecasting model for monthly rainfall precipitation,” Adv. Appl. Stat., 2023, https://doi.org/10.17654/0972361723066.
M. H. Alsharif, S. Kim, and J. Kim, “Long-term rainfall forecasting in arid climates using artificial intelligence and statistical recurrent models,” J. Eng. Res., vol. 13, no. 2, pp. 1594–1602, 2025, https://doi.org/10.1016/j.jer.2024.03.001.
N. N. Aini, A. Iriany, W. H. Nugroho, and F. L. Wibowo, “Comparison of adaptive Holt-Winters exponential smoothing and recurrent neural network model for forecasting rainfall in Malang City,” ComTech Comput. Math. Eng. Appl., vol. 13, no. 2, pp. 87–96, 2022, https://doi.org/10.21512/comtech.v13i2.7570.
W. Thupeng, R. Sivasamy, and O. A. Daman, “Rainfall series forecasting models by ARIMA, NN, and HOMM methods,” Adv. Appl. Stat., 2024, https://doi.org/10.17654/0972361724007.