Rainfall Forecasting Using Historical Rainfall and Humidity Data in Wonorejo Watershed, Indonesia
DOI:
https://doi.org/10.30736/cvl.v11i1.1645Keywords:
Rainfall forecasting, Decomposition method, Regression analysis, Nonlinear Regression, Water resource management, Wonorejo WatershedAbstract
Rainfall prediction plays a crucial role in the design, planning, and management of water resource systems. This study addresses the importance of accurate rainfall forecasting to optimize the operational efficiency of the Wonorejo Reservoir in Tulungagung Regency, Indonesia. By improving rainfall predictions, it becomes possible to estimate available water volumes more precisely and enhance reservoir utilization beyond current practices. This research employs three analytical approaches—Decomposition, Multiple Linear Regression, and Nonlinear Regression—to forecast rainfall in the Wonorejo Watershed. The models are developed using monthly total rainfall and average air humidity data collected from January 1998 to December 2018. Model performance is evaluated using the Root Mean Square Error (RMSE). The Nonlinear Regression method yields the lowest RMSE (72.71), followed by Decomposition (73.78) and Multiple Linear Regression (103.57). Based on these results, the Nonlinear Regression model is identified as the most suitable approach for forecasting rainfall in the Wonorejo Basin for the subsequent 24 months. This study recommends the application of the Nonlinear Regression method for rainfall forecasting in this specific case study area.
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[1] D. C. Siregar, “Simulasi Prediksi Total Hujan Bulanan di Tanjungpinang (Studi Kasus Tahun 2017),” J. Stat. dan Apl., vol. 2, no. 2, pp. 1–7, 2018, doi: 10.21009/jsa.02201.
[2] S. S. Chinchorkar, G. R. Patel, and F. G. Sayyad, “Development of monsoon model for long range forecast rainfall explored for Anand ( Gujarat-India ),” Int. J. Water Resour. Environ. Eng., vol. 4, no. 11, pp. 322–326, 2012, doi: 10.5897/IJWREE11.097.
[3] E. G. Union, “Interactive comment on ‘ Downscaled Rainfall Prediction Model ( DRPM ) using a Unit Disaggregation Curve ( UDC )’ by S . Tantanee et al .,” 2005.
[4] A. Fadholi, A., Persamaan regresi prediksi curah hujan bulanan menggunakan data suhu dan kelembapan udara di Ternate,13 (1) 2013, pp. 7–16 [Online]. Available: https://ejournal.unisba.ac.id/index.php/statistika/article/view/1068.
[5] Hettiarachchi, P., Hall, M. J., Minns, A. W., & Hettiarachchi, A. P., The extrapolation of artificial neural networks for the modelling of rainfall-runoff relationships, Journal of Hydroinformatics, 07(4), 2005, pp. 291-296.
[6] Sloughter, J. M. L., Raftery, A. E., Gneiting, T., & Fraley, C., Probabilistic quantitative precipitation forecasting using bayesian model averaging. Monthly Weather Review, 135(9), 2007, pp. 3209–3220. https://doi.org/10.1175/MWR3441.1
[7] Wijayarathne, D., Coulibaly, P., Boodoo, S., & Sills, D., Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting, Journal of Hydrometeorology, 22(8), 2021, pp. 2033–2053. https://doi.org/10.1175/JHM-D-20-0267.1
[8] Huria, A., Kamboj, G., Kukreti, D., & Rawat, J., A Relative Analysis of Modern ML Methods for Rainfall Prediction, Proceedings of the Advancement in Electronics & Communication Engineering, 2022. http://dx.doi.org/10.2139/ssrn.4159466
[9] Wang, D., Huo, Z., Miao, P., Tian, X., Comparison of Machine Learning Models to Predict Lake Area in an Arid Area, Remote Sensing, 15(17), 2023, pp.4153. https://doi.org/10.3390/rs15174153
[10] Wang, J., Wong, R. K. W., Jun, M., Schumacher, C., Saravanan, R., & Sun, C., Statistical and machine learning methods applied to the prediction of different tropical rainfall types. Environ. Res. Commun, 3, 2021.
[11] Laddimath, R. S., Patil, N. S., and Hooli, S., Downscaling of Precipitation Data from GCM outputs using Artificial Neural Network for Bhima basin, International Journal of Applied Environmental Sciences, 10 (4), 2015, pp. 1493-1508.
[12] Bishara, A. J., Hittner, J. B., Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality, Educ Psychol Meas, 75(5), 2015, pp. 785-804. doi: 10.1177/0013164414557639.
[13] Santoso, S., Panduan Lengkap Menguasai Statistik dengan SPSS 17, PT Elex Media Komputindo, Jakarta, 2009.
[14] Margini, N. F, Damarnegara, S., Anwar, N., Yusop., Z, Water Allocation in Multi-Purpose and Multi-Year Reservoir using Ant Colony Optimization. Sustainable Water Resources Management, Vol. 10, 2024. doi : 10.1007/s40899-024-01093-4.
[15] Indarto, I. Review of the Application of Hydrological Change Indicators in the Wonorejo Watershed, Journal of Agricultural Engineering, 6 (3), 2018, , pp. 241-248.
[16] Wilks, D.S., Statistical Methods in the Atmospheric Sciences, Academic Press, San Diego, pp. 467, 1995.
[17] Kartasapoetra, A. G, et. all., Klimatologi: Pengaruh Iklim Terhadap Tanah dan Tanaman, Bumi Aksara, Jakarta, 2012.
[18] Rezaeianzadeh, M., Tabari,H., Arabi Yazdi, A., Isik. S., and Kalin, L., Flood flow forecasting using ANN, ANFIS and regression models, Neural Comput. Appl., 25 (1), pp. 25–37, 2014, doi: 10.1007/s00521-013-1443-6.
[19] M. Rezaeianzadeh, H. Tabari, A. Arabi Yazdi, S. Isik, and L. Kalin, “Flood flow forecasting using ANN, ANFIS and regression models,” Neural Comput. Appl., vol. 25, no. 1, pp. 25–37, 2014, doi: 10.1007/s00521-013-1443-6.
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