A Climatology-Relative Framework for Evaluating Rainfall-Condition Categories Using Observed and Random Forest-Predicted Dasarian Rainfall
-
Selina Ayuningtyas
*
Universitas Islam Negeri Maulana Malik Ibrahim, Malang,
Indonesia
- Yunifa Miftachul Arif Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia
- Fresy Nugroho Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia
- Usman Pagalay Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia
- Triyo Supriyatno Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia (*) Corresponding Author
Abstract
Rainfall variability strongly influences rain-fed agricultural activities in monsoonal regions, particularly across seasonal and spatially varying rainfall regimes. This study develops and applies a climatology-relative framework for evaluating rainfall-condition categories derived from observed and Random Forest (RF)-predicted dasarian rainfall for Besuki Na-Oogst tobacco in ZOM 45–49, Jember, East Java. A 30-year historical rainfall dataset (1993–2022) was used to derive ZOM- and dasarian-specific P25 and P75 relative rainfall-condition thresholds. The same fixed thresholds were then applied to observed and RF-predicted rainfall throughout January–December 2024 to classify rainfall into Dry, Normal, and Wet categories. Categorical correspondence was evaluated using percentage agreement, Cohen's kappa, and a 3 × 3 confusion matrix at the ZOM × dasarian level. Across 180 ZOM × dasarian pairs, observed rainfall produced 50 Dry, 84 Normal, and 46 Wet classifications, whereas RF-predicted rainfall produced 7 Dry, 68 Normal, and 105 Wet classifications. Overall categorical agreement was 38.33% (κ = 0.071), with variation among the five ZOMs. The most pronounced discrepancy occurred in the Dry category: none of the 50 observed Dry cases were reproduced as Dry by the RF predictions, while 36 were classified as Wet. Conversely, all seven RF-predicted Dry cases corresponded to observed Wet conditions. The continuous comparison indicated that the RF predictions generally reproduced the broad seasonal rainfall pattern but tended to exhibit smoother variations and, in several periods, lower rainfall magnitudes than observed. These differences can lead to categorical reversals when predicted values fall across fixed climatological P25 and P75 boundaries. The findings indicate that RF-derived rainfall-condition patterns may provide complementary information for climatology-relative assessment, but should not be interpreted as direct substitutes for observed rainfall conditions or as standalone planting recommendations. The proposed framework complements conventional numerical rainfall-prediction evaluation by assessing the categorical representation of predicted rainfall within a common local historical climatology at the ZOM × dasarian scale.
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References
S. Kumar et al., “Rainfall variability for crop water management under changing climate in Himachal Pradesh,” Appl. Water Sci., vol. 15, no. 12, 2025, doi: 10.1007/s13201-025-02653-5.
N. M. M. Candra Devi, I. P. A. Bayupati, and N. K. A. Wirdiani, “Prediksi Curah Hujan Dasarian dengan Metode Vanilla RNN dan LSTM untuk Menentukan Awal Musim Hujan dan Kemarau,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 8, no. 3, p. 405, 2022, doi: 10.26418/jp.v8i3.56606.
Z. N. Ruslana, R. Tresnawati, R. Rosyidah, I. W. Harmoko, and S. Siswanto, “Reliabilitas Prediksi Curah Hujan Dasarian Pada Kejadian Curah Hujan Ekstrim Pemicu Banjir 26 Oktober 2020 di Kebumen: Model Statistik (HyBMG) versus Model Dinamik (ECMWF),” Jurnal Geosains dan Teknologi, vol. 4, no. 2, pp. 83–100, 2021, doi: 10.14710/jgt.4.2.2021.83-100.
C. Handika, K. Winardi, F. Arifianto, Y. Darmawan, and E. Ferdiansyah, “Evaluasi Model Long Short-Term Memory (Lstm) Untuk Prediksi Curah Hujan Di Zona Musim (Zom) Provinsi Jawa Tengah,” Inovasi Fisika Indonesia, vol. 15, no. 2, pp. 337–351, 2026, [Online]. Available: https://ejournal.unesa.ac.id/index.php/inovasi-fisika-indonesia/article/view/82693
I. Harlianingtyas, C. Triwidiarto, and S. I. Kusuma, “The Impact of Climate on Tobacco Production in Jember Regency,” Scientific Journal of Innovation, vol. 21, no. 2, pp. 86–94, 2021.
I. Harlianingtyas, M. Azizah, S. I. Kusuma, Muksin, and S. Humaida, “Modeling the effect of climate on na-oogst tobacco production in Jember,” IOP Conf. Ser. Earth Environ. Sci., vol. 980, no. 1, 2022, doi: 10.1088/1755-1315/980/1/012005.
R. C. North, M. P. Mittermaier, and S. F. Milton, “Using SEEPS with a TRMM-Derived Climatology to Assess Global NWP Precipitation Forecast Skill,” Mon. Weather Rev., vol. 150, no. 1, pp. 135–155, 2022, doi: 10.1175/MWR-D-20-0347.1.
Z. Ben-Bouallègue, A. Prieto-Nemesio, A. Iza Wong, F. Pinault, M. van der Schee, and U. Modigliani, “SEEPS4ALL: an open dataset for the verification of daily precipitation forecasts using station climate statistics,” Earth Syst. Sci. Data, vol. 18, no. 1, pp. 713–720, 2026, doi: 10.5194/essd-18-713-2026.
J. S. R. Pirret, J. D. Daron, P. E. Bett, N. Fournier, and A. K. Foamouhoue, “Assessing the skill and reliability of seasonal climate forecasts in Sahelian West Africa,” Weather Forecast., vol. 35, no. 3, pp. 1035–1050, 2020, doi: 10.1175/WAF-D-19-0168.1.
F. Calì Quaglia, S. Terzago, and J. von Hardenberg, “Temperature and precipitation seasonal forecasts over the Mediterranean region: added value compared to simple forecasting methods,” Clim. Dyn., vol. 58, no. 7–8, pp. 2167–2191, 2022, doi: 10.1007/s00382-021-05895-6.
C. M. Castellano et al., “Development of a Statistical Subseasonal Forecast Tool to Predict California Atmospheric Rivers and Precipitation Based on MJO and QBO Activity,” Journal of Geophysical Research: Atmospheres, vol. 128, no. 6, pp. 1–19, 2023, doi: 10.1029/2022JD037360.
Z. Li, J. E. Smerdon, R. Seager, N. Siegert, and J. S. Mankin, “Emergent Trends Complicate the Interpretation of the United States Drought Monitor (USDM),” AGU Advances, vol. 5, no. 2, 2024, doi: 10.1029/2023AV001070.
M. Kendon et al., “State of the UK Climate 2023,” International Journal of Climatology, vol. 44, no. S1, pp. 1–117, 2024, doi: 10.1002/joc.8553.
C. Liu, J. Sun, X. Yang, S. Jin, and S. Fu, “Evaluation of ecmwf precipitation predictions in china during 2015–18,” Weather Forecast., vol. 36, no. 3, pp. 1043–1060, 2021, doi: 10.1175/WAF-D-20-0143.1.
M. F. Wehner, M. L. Duffy, M. Risser, C. J. Paciorek, D. A. Stone, and P. Pall, “On the uncertainty of long-period return values of extreme daily precipitation,” Frontiers in Climate, vol. 6, no. March, 2024, doi: 10.3389/fclim.2024.1343072.
J. R. Lanzante, K. W. Dixon, D. Adams-Smith, M. J. Nath, and C. E. Whitlock, “Evaluation of some distributional downscaling methods as applied to daily precipitation with an eye towards extremes,” International Journal of Climatology, vol. 41, no. 5, pp. 3186–3202, 2021, doi: 10.1002/joc.7013.
E. Williams, C. Funk, P. Peterson, and C. Tuholske, “High resolution climate change observations and projections for the evaluation of heat-related extremes,” Sci. Data, vol. 11, no. 1, pp. 1–17, 2024, doi: 10.1038/s41597-024-03074-w.
Ashari Rakhmat Galih and Mutohar Wisnu., “Prakiraan Hujan menggunakan Metode Random Forest dan Cross Validation,” Journal MIND Journal | ISSN, vol. 8, no. 2, pp. 173–187, 2023, [Online]. Available: https://doi.org/10.26760/mindjournal.v8i2.173-187
V. Kumar, N. Kedam, O. Kisi, S. Alsulamy, K. M. Khedher, and M. A. Salem, “A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting,” Water Resources Management, vol. 39, no. 1, pp. 271–290, 2025, doi: 10.1007/s11269-024-03969-8.
D. A. Lavers, A. Simmons, F. Vamborg, and M. J. Rodwell, “An evaluation of ERA5 precipitation for climate monitoring,” Quarterly Journal of the Royal Meteorological Society, vol. 148, no. 748, pp. 3152–3165, 2022, doi: 10.1002/qj.4351.
A. F. D. Putra, M. N. Azmi, H. Wijayanto, S. Utama, and I. G. P. W. Wedashwara Wirawan, “Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 3, pp. 519–530, 2024, doi: 10.30812/matrik.v23i3.3891.
H. Akbar and W. K. Sanjaya, “Kajian Performa Metode Class Weight Random Forest pada Klasifikasi Imbalance Data Kelas Curah Hujan,” Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi, vol. 3, no. 1, 2023, doi: 10.20885/snati.v3i1.30.
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