A Hybrid ACO-BPNN-XGBoost Model for Monthly Rainfall Time-Series Forecasting
DOI:
https://doi.org/10.63158/journalisi.v8i4.1693Keywords:
Rainfall Forecasting, Hybrid Model, Ant Colony Optimization, Backpropagation Neural Network, XGBoostAbstract
This study aimed to develop and evaluate a hybrid forecasting model integrating Ant Colony Optimization (ACO), Backpropagation Neural Network (BPNN), and XGBoost for monthly rainfall prediction. The proposed hybrid framework combines optimization, neural network, and boosting techniques within a single forecasting model. Monthly rainfall time-series data from 2016 to 2025 in Alas Subdistrict, Sumbawa Regency, West Nusa Tenggara, were used, comprising 120 observations obtained from BPS, BMKG, and NASA POWER. The methodology included data preprocessing, an 80%–20% chronological training–testing split, model development, and performance evaluation using MSE, MAE, RMSE, MAPE, and R². The results indicated that all models experienced performance degradation during testing, suggesting overfitting and limited generalization capability. The testing RMSE values for ACO, BPNN, XGBoost, and the hybrid model were 108.41, 109.33, 109.21, and 106.08 mm, respectively. The corresponding testing MAPE values were 2836.5%, 2900.5%, 2073.9%, and 2602.2%, although these values should be interpreted cautiously because rainfall observations occasionally approached zero. While the hybrid model achieved the lowest testing RMSE, the improvement over the best individual model was modest, and all testing R² values remained negative, indicating weak generalization capability. Therefore, the findings should be regarded as preliminary, and further validation using larger datasets, exogenous climatic predictors, baseline forecasting methods, and more rigorous evaluation procedures is required.
Downloads
References
[1] A. M. Elshewey, A. A. Alhussan, and D. S. Khafaga, “An enhanced adaptive dynamic metaheuristic optimization algorithm for rainfall prediction depends on long short-term memory,” PLoS One, vol. 20, no. 6, p. e0317554, 2025, doi: 10.1371/journal.pone.0317554.
[2] M. N. Tahroudi, “Comprehensive global assessment of precipitation trend and pattern variability considering their distribution dynamics,” Sci. Rep., vol. 15, no. 1, pp. 1–19, 2025, doi: 10.1038/s41598-025-06050-5.
[3] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. Melbourne, Australia: OTexts, 2021. [Online]. Available: https://otexts.com/fpp3/
[4] M. Arief, F. Istiyanto, and P. Palloan, “Monthly Rainfall Prediction Using the Backpropagation Neural Network ( BPNN ) Algorithm in Maros Regency,” Sci. J. Informatics, vol. 10, no. 1, pp. 13–24, 2025, doi: 10.15294/sji.v10i1.37982.
[5] E. M. Fischer and R. Knutti, “Observed heavy precipitation increase confirms theory and early models,” Nat. Clim. Chang., vol. 6, no. 11, pp. 986–991, 2016, doi: 10.1038/nclimate3110.
[6] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Hoboken, NJ, USA: John Wiley & Sons, 2015.
[7] M. Ehsani, A. Zarei, H. Gupta, K. Barnard, and A. Behrangi, “NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks,” IEEE Trans. Geosci. Remote Sens., vol. 60, no. 4706021, pp. 1–21, 2021, doi: 10.1109/TGRS.2022.3158888.
[8] B. Oldfield and S. Kandanaarachchi, “SoftwareX An Item Response Theory-based R module for Algorithm Portfolio Analysis,” SoftwareX, vol. 31, no. 1, p. 102239, 2025, doi: 10.1016/j.softx.2025.102239.
[9] J. E. Nash and J. V Sutcliffe, “River flow forecasting through conceptual models Part I—A discussion of principles,” J. Hydrol., vol. 10, no. 3, pp. 282–290, 1970, doi: 10.1016/0022-1694(70)90255-6.
[10] I. Aljarah, H. Faris, and S. Mirjalili, “Optimizing connection weights in neural networks using the whale optimization algorithm,” Soft Comput., vol. 22, no. 1, pp. 1–15, 2018, doi: 10.1007/s00500-016-2442-1.
[11] S. Renuga Devi, P. Arulmozhivarman, C. Venkatesh, and P. Agarwal, “Performance comparison of artificial neural network models for daily rainfall prediction,” Int. J. Autom. Comput., vol. 13, no. 5, pp. 417–427, 2016, doi: 10.1007/s11633-016-0986-2.
[12] 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. 1–10, 2023, doi: 10.15294/sji.v10i1.37982.
[13] H. V Gupta, H. Kling, K. K. Yilmaz, and G. F. Martinez, “Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling,” J. Hydrol., vol. 377, no. 1–2, pp. 80–91, 2009, doi: 10.1016/j.jhydrol.2009.08.003.
[14] S. S. Sammen et al., “Rainfall modeling using two different neural networks improved by metaheuristic algorithms,” Environ. Sci. Eur., vol. 35, no. 1, pp. 1–25, 2023, doi: 10.1186/s12302-023-00818-0.
[15] S. O. Oyedepo et al., “A critical review on enhancement and sustainability of energy systems : perspectives on thermo-economic and thermo-environmental analysis,” vol. 12, no. 1417453, pp. 1–29, 2025, doi: 10.3389/fenrg.2024.1417453.
[16] D. N. Moriasi, J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations,” Trans. ASABE, vol. 50, no. 3, pp. 885–900, 2007, doi: 10.13031/2013.23153.
[17] S. Madadgar et al., “A hybrid statistical-dynamical framework for meteorological drought prediction: Application to the southwestern United States,” Water Resour. Res., vol. 52, no. 7, pp. 5095–5110, 2016, doi: 10.1002/2015WR018547.
[18] H. Chen, V. Chandrasekar, H. Tan, and R. Cifelli, “Rainfall Estimation From Ground Radar and TRMM Precipitation Radar Using Hybrid Deep Neural Networks,” Geophys. Res. Lett., vol. 46, no. 17–18, pp. 10669–10678, 2019, doi: 10.1029/2019GL084771.
[19] S. Zhang, T. Chang, and D. Lin, “A Preliminary Study on a Hybrid Wavelet Neural Network Model for Forecasting Monthly Rainfall,” vol. 14, no. 5, pp. 1747–1757, 2018, doi: 10.29333/ejmste/85119.
[20] V. Kumar, N. Kedam, and O. Kisi, “A comparative study of machine learning models for daily and weekly rainfall forecasting,” Water Resour. Manag., vol. 39, no. 1, pp. 271–290, 2025, doi: 10.1007/s11269-024-03969-8.
[21] J. F. Torres, D. Hadjout, A. Sebaa, F. Martínez-Álvarez, and A. Troncoso, “Deep Learning for Time Series Forecasting: A Survey,” Big Data, vol. 9, no. 1, pp. 3–34, 2021, doi: 10.1089/big.2020.0159.
[22] Y. Wang, J. Liu, R. Li, X. Suo, and E. Lu, “Precipitation forecast of the Wujiang River Basin based on artificial bee colony algorithm and backpropagation neural network,” Alexandria Eng. J., vol. 59, no. 3, pp. 1473–1483, 2020, doi: 10.1016/j.aej.2020.04.035.
[23] M. Usman, S. Khan, K. Mohammad, and A. Hussain, “Results in Engineering Comparative analysis of different rainfall prediction models : A case study of Aligarh City, India,” Results Eng., vol. 22, no. 01, p. 102093, 2024, doi: 10.1016/j.rineng.2024.102093.
[24] S. Dotse, I. Larbi, A. Limantol, and L. De Silva, “A review of the application of hybrid machine learning models to improve rainfall prediction,” Model. Earth Syst. Environ., vol. 10, no. 1, pp. 19–44, 2023, doi: 10.1007/s40808-023-01835-x.
[25] A. D. Gorgij, O. Kisi, and S. Heddam, “Metaheuristic-optimized neuro-fuzzy models for meteorological drought prediction,” Environ. Earth Sci., vol. 85, no. 192, pp. 1–24, 2026, doi: 10.1007/s12665-026-12910-8.
[26] S. A. Sahaar and J. D. Niemann, “Estimating rootzone soil moisture by fusing multiple remote sensing products with machine learning,” Remote Sens., vol. 16, no. 19, p. 3699, 2024, doi: 10.3390/rs16193699.
[27] C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artif. Intell. Rev., vol. 54, no. 3, pp. 1937–1967, 2021, doi: 10.1007/s10462-020-09896-5.
[28] H. Li, Y. Zhang, H. Lei, and X. Hao, “Machine learning-based bias correction of precipitation measurements at high altitude,” Remote Sens., vol. 15, no. 8, p. 2180, 2023, doi: 10.3390/rs15082180.
[29] K. Budholiya, S. Shrivastava, and V. Sharma, “An optimized XGBoost based diagnostic system for effective prediction of heart disease,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 7, pp. 4514–4523, 2020, doi: 10.1016/j.jksuci.2020.10.013.
[30] S. Kolassa, “Why the Mean Absolute Percentage Error Should Not Be Used for Forecast Accuracy Measurement,” Foresight Int. J. Appl. Forecast., no. 61, pp. 24–33, 2020.
[31] M. Dorigo and L. M. Gambardella, “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 53–66, 1997, doi: 10.1109/4235.585892.
[32] C. Bergmeir, R. J. Hyndman, and B. Koo, “A Note on the Validity of Cross-Validation for Evaluating Autoregressive Time Series Prediction,” Comput. Stat. Data Anal., vol. 120, pp. 70–83, 2018, doi: 10.1016/j.csda.2017.11.003.
[33] R. J. Hyndman and A. B. Koehler, “Another Look at Measures of Forecast Accuracy,” Int. J. Forecast., vol. 22, no. 4, pp. 679–688, 2006, doi: 10.1016/j.ijforecast.2006.03.001.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
