Performance Comparison of Recursive and Semi-Recursive Random Forest Models for Monthly Rainfall Prediction in the Bogowonto Watershed
Abstract
Unpredictable rainfall is considered a major challenge for agricultural systems in Indonesia, especially in the preparation of planting calendars. Therefore, accurate rainfall predictions are essential for agricultural systems to be more adaptive to climate change. The aim of this study is to analyse and compare the accuracy of two monthly rainfall prediction schemes, namely recursive and semi-recursive approaches, by using the Random Forest algorithm. Climatological data from four stations in the Bogowonto Watershed were used, and the modelling process included data pre-processing, feature engineering (lag, rolling window, and seasonal transformation), and grid search with cross-validation to obtain the optimal parameter combinations. Model performance was evaluated on out-of-sample test and validation data by using RMSE, MAE, NSE, and R². The semi-recursive approach improved NSE from 0.20–0.33 to 0.53–0.61 and reduced RMSE from 210.68–255.75 mm to 150.79–211.31 mm, while R² values increased from 0.23–0.50 to 0.72–0.82 across the four stations. These results indicate that the semi-recursive approach is more stable in predicting monthly rainfall and thus is recommended for planning a planting calendar in Bogowonto Watershed.
Downloads
References
Adawyah, R., Soetikno, N., & Aisyah, S. (2024). Ikan gabus: Ragam produk olahannya. Penerbit BRIN. https://doi.org/10.55981/brin.726
Ahmed, K., Shahid, S., Ismail, T., Nawaz, N., & Wang, X.J. (2018). Absolute homogeneity assessment of precipitation time series in an arid region of Pakistan. Atmosfera, 31(3), 301–316. https://doi.org/10.20937/ATM.2018.31.03.06
Amin Burhanuddin, S.N.Z., Mohd Deni, S., & Mohamed Ramli, N. (2016). Revised normal ratio methods for imputation of missing rainfall data. Scientific Research Journal, 13(1), 83–97. https://doi.org/10.24191/srj.v13i1.9384
Aslam, F.M., & Afghani, F.A. (2024). Comparing monthly rainfall prediction in West Sumatra using SARIMA, ETS, LSTM, and XGBoosting methods. Indonesian Journal of Applied Statistics, 7(1), 14–26. https://doi.org/10.13057/ijas.v7i1.83187
Boult, V.L., Asfaw, D.T., Young, M., Maidment, R., Mwangi, E., Ambani, M., Waruru, S., Otieno, G., Todd, M.C., & Black, E. (2020). Evaluation and validation of TAMSAT-ALERT soil moisture and WRSI for use in drought anticipatory action. Meteorological Applications, 27(5), 1959. https://doi.org/10.1002/met.1959
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Che Rose, F.Z., Rosili, N.A.K., & Marsani, M.F. (2025). Comparison of machine learning model performance for predicting the climate variables in Johor Bahru, Malaysia. Scientific Reports, 15, 23465. https://doi.org/10.1038/s41598-025-08033-y
Chinasho, A., Bedadi, B., Lemma, T., Tana, T., Hordofa, T., & Elias, B. (2021). Evaluation of seven gap-filling techniques for daily station-based rainfall datasets in South Ethiopia. Advances in Meteorology, 2021. https://doi.org/10.1155/2021/9657460
Chow, V.T., Maidment, D.R., & Mays, L.W. (1988). Applied hydrology. McGraw-Hill.
Đurin, B., Kranjčić, N., Kanga, S., Singh, S.K., Sakač, N., Pham, Q.B., Hunt, J., Dogančić, D., & Nunno, F.D. (2022). Application of rescaled adjusted partial sums (RAPS) method in hydrology – an overview. Advances in Civil and Architectural Engineering, 13(25), 58–72. https://doi.org/10.13167/2022.25.6
Ejike, O., Ndzi, D., & Shakir, M.Z. (2025). Comparative study of machine learning-based rainfall prediction in tropical and temperate climates. Climate, 13(8), 167. https://doi.org/10.3390/cli13080167
El Hafyani, M., El Himdi, K., & El Adlouni, S.E. (2024). Improving monthly precipitation prediction accuracy using machine learning models: a multi-view stacking learning technique. Frontiers in Water, 6, 1378598. https://doi.org/10.3389/frwa.2024.1378598
Gunawan, G., Andriani, W., & Akbar, A.A. (2024). Application of machine learning for short-term climate prediction in Indonesia. Jurnal Mantik, 8(1), 828–837. https://doi.org/10.35335/mantik.v8i1.5215
Hadeed, S.J., O’Rourke, M.K., Burgess, J.L., Harris, R.B., & Canales, R.A. (2020). Imputation methods for addressing missing data in short-term monitoring of air pollutants. Science of the Total Environment, 730, 139140. https://doi.org/10.1016/j.scitotenv.2020.139140
Hoogenboom, G., Porter, C.H., Boote, K.J., Shelia, V., Wilkens, P.W., Singh, U., White, J.W., Asseng, S., Lizaso, J.I., Moreno, L. P., Pavan, W., Ogoshi, R., Hunt, L.A., Tsuji, G.Y., & Jones, J.W. (2019). The DSSAT crop modeling ecosystem. In K.J. Boote (Ed.), Advances in crop modeling for a sustainable agriculture (pp. 173–216). Burleigh Dodds Science Publishing. https://doi.org/10.19103/AS.2019.0061.10
Mair, A., & Fares, A. (2011). Comparison of rainfall interpolation methods in a mountainous region of a tropical island. Journal of Hydrologic Engineering, 16(4), 371–383. https://doi.org/10.1061/(asce)he.1943-5584.0000330
Mbenza, W., & Sho, K. (2023). A machine learning-based approach to capture extreme rainfall events. International Journal of Applied Engineering & Technology, 5(4), 1983–1993.
Mondo, J.M., Chuma, G.B., Matiti, H.M., Kihye, J.B., Bagula, E.M., Karume, K., Kahindo, C., Egeru, A., Majaliwa, J.G.M., Agre, P.A., Adebola, P.A., & Asfaw, A. (2024). Crop calendar optimization for climate change adaptation in yam farming in South-Kivu, eastern D.R. Congo. PLoS ONE, 19(9). https://doi.org/10.1371/journal.pone.0309775
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., & Veith, T.L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153
Ogunniyi, J.A., Elbasit, M.A.M.A., & Obagbuwa, I.C. (2024). Monthly rainfall prediction for different climatic zones in South Africa for 2024 using a random forest model. Edelweiss Applied Science and Technology, 8(6), 1805–1827. https://doi.org/10.55214/25768484.v8i6.2347
Oliveira, E.C.L., Carvalho, E.C., Jesus, E.S., Rocha, R.L., Arruda, H.M., Alves, R.C.O., & Tedeschi, R.G. (2025). A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city. Frontiers in Earth Science, 13, 1589753. https://doi.org/10.3389/feart.2025.1589753
Onyutha, C. (2024). Pros and cons of various efficiency criteria for hydrological model performance evaluation. Proceedings of the International Association of Hydrological Sciences, 385, 181–187. https://doi.org/10.5194/piahs-385-181-2024
Pal, M., Maity, R., Ratnam, J.V., Nonaka, M., & Behera, S.K. (2020). Long-lead prediction of ENSO Modoki index using machine learning algorithms. Scientific Reports, 10, 365. https://doi.org/10.1038/s41598-019-57183-3
Permata, R., Muhaimin, A., & Hidayati, S. (2024). Rainfall forecasting with an intermittent approach using hybrid exponential smoothing neural network. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 18(1), 0457-0466. https://doi.org/10.30598/barekengvol18iss1pp0457-0466
Pettitt, A.N. (1979). A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(2), 126–135. https://doi.org/10.2307/2346729
Primajaya, A., & Sari, B.N. (2018). Random forest algorithm for prediction of precipitation. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), 1(1), 27–31. http://dx.doi.org/10.24014/ijaidm.v1i1.4903
Putra, A.F.D., Azmi, M.N., Wijayanto, H., Utama, S., & Wedashwara Wirawan, I.G.P.W. (2024). Optimizing rain prediction model using random forest and grid search cross-validation for agriculture sector. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 23(3), 519–530. https://doi.org/10.30812/matrik.v23i3.3891
Rakhmat, G.A., & Mutohar, W. (2023). Prakiraan hujan menggunakan metode random forest dan cross validation. MIND (Multimedia Artificial Intelligent Networking Database) Journal, 8(2), 173–187.
Ribeiro, S., Caineta, J., & Costa, A.C. (2016). Review and discussion of homogenisation methods for climate data. Physics and Chemistry of the Earth, Parts A/B/C, 94, 167–179. https://doi.org/10.1016/j.pce.2015.08.007
Sulistyowati, I.D., Sunarno, S., Iqbal, I., & Syamsuri, K.M.N. (2025). Application of feature selection and comparative analysis of machine learning models for rainfall prediction in Jakarta. Journal of Applied Informatics and Computing (JAIC), 9(5), 2364–2370. https://doi.org/10.30871/jaic.v9i5.11000
Waqas, M., Humphries, U.W., & Hlaing, P.T. (2024). Time series trend analysis and forecasting of climate variability using deep learning in Thailand. Results in Engineering, 24, 102997. https://doi.org/10.1016/j.rineng.2024.102997
Zaveri, E., Russ, J., & Damania, R. (2020). Rainfall anomalies are a significant driver of cropland expansion. Proceedings of the National Academy of Sciences, 117(19), 10225–10233. https://doi.org/10.1073/pnas.1910719117
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
