AI-Driven Ensemble Learning for Spatio-Temporal Rainfall Prediction in the Bengawan Solo River Watershed, Indonesia

Jumadi Jumadi (), Danardono Danardono, Efri Roziaty, Agus Ulinuha, Supari Supari, Lam Kuok Choy, Farha Sattar and Muhammad Nawaz
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Jumadi Jumadi: Faculty of Geography, Universitas Muhammadiyah Surakarta, Surakarta 57162, Indonesia
Danardono Danardono: Faculty of Geography, Universitas Muhammadiyah Surakarta, Surakarta 57162, Indonesia
Efri Roziaty: Faculty of Education, Universitas Muhammadiyah Surakarta, Surakarta 57162, Indonesia
Agus Ulinuha: Faculty of Technology, Universitas Muhammadiyah Surakarta, Surakarta 57162, Indonesia
Supari Supari: Meteorology, Climatology, and Geophysics Agency, Central Jakarta 10720, Indonesia
Lam Kuok Choy: Geography Program, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Farha Sattar: Faculty of Arts & Society, Education & Enabling, Charles Darwin University, Ellengowan Drive, Casuarina, Darwin 0810, Australia
Muhammad Nawaz: Department of Geography, National University of Singapore, 1 Arts Link, Block AS2, Singapore 117570, Singapore

Sustainability, 2025, vol. 17, issue 20, 1-21

Abstract: Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction models. This study introduces an innovative approach by applying ensemble stacking, which combines machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Light Gradient-Boosting Machine (LGBM) and deep learning models like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Networks (TCN), Convolutional Neural Network (CNN), and Transformer architecture based on monthly Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data (1981–2024). The novelty of this research lies in the systematic exploration of various model combination scenarios—both classical and deep learning and the evaluation of their performance in projecting rainfall for 2025–2030. All base models were trained on the 1981–2019 period and validated with data from the 2020–2024 period, while ensemble stacking was developed using a linear regression meta-learner. The results show that the optimal ensemble scenario reduces the MAE to 53.735 mm, the RMSE to 69.242 mm, and increases the R 2 to 0.795826—better than all individual models. Spatial and temporal analyses also indicate consistent model performance at most locations and times. Annual rainfall projections for 2025–2030 were then interpolated using IDW to generate a spatio-temporal rainfall distribution map. The improved accuracy provides a strong scientific basis for disaster preparedness, flood and drought management, and sustainable water planning in the Bengawan Solo River Watershed. Beyond this case, the approach demonstrates significant transferability to other climate-sensitive and data-scarce regions.

Keywords: ensemble stacking; spatio-temporal rainfall prediction; machine learning; deep learning; CHIRPS data; hydrometeorological forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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