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Muhajirin, Muhajirin
Teknik Informatika, Universitas Teknologi Akba Makassar
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Mathematics Public Health Social Sciences Other

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Explainable Machine Learning for Long-Term Monthly Hydroclimatic Forecasting and Extreme-Event Detection Arif Fadillah; Markani Pato; Nuraida Latif; Benny Leornard Encrico Panggabean; Muhammad Rizal; Mursalim Mursalim; Muhajirin Muhajirin
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2741

Abstract

Long-term hydroclimatic prediction in arid urban environments remains methodologically demanding because monthly records are often intermittent, highly seasonal, zero-inflated, and dominated by rare but consequential extreme events. Using a 121-year monthly hydroclimatic record for Makkah, Saudi Arabia, spanning January 1901 to December 2021, this study develops an explainable hybrid machine-learning framework for monthly forecasting, seasonal diagnostics, and extreme-event detection. The dataset contains 1,452 monthly observations with a mean value of 6.19, median of 3.00, standard deviation of 8.05, and maximum of 52.00, indicating a strongly skewed distribution. Exploratory analysis reveals pronounced seasonality: November, December, and January exhibit the highest hydroclimatic values, whereas June is consistently dry across the full record. A temporal feature set was constructed using lag variables, rolling statistics, annual seasonal memory, cyclical month encodings, and trend indicators. Several predictive models were evaluated, including Random Forest, Extra Trees, Histogram Gradient Boosting, XGBoost, and a hybrid SARIMA–Random Forest residual-correction model. Extra Trees achieved the best forecasting performance on the holdout period, with MAE = 2.997, RMSE = 5.603, sMAPE = 57.669%, and R² = 0.518. Extreme-event detection was performed using a 90th-percentile threshold of 17.68, identifying 146 extreme months over the full record. The best classification trade-off was obtained by Histogram Gradient Boosting, while Random Forest produced the highest ROC-AUC. SHAP-based interpretation demonstrates that seasonal phase variables and annual memory features dominate model behaviour, especially month_cos, month_sin, same_month_last_year, and lag_12. The findings show that interpretable ensemble learning can provide a more transparent and operationally relevant framework than accuracy-only forecasting for arid-region hydroclimatic risk assessment.
Co-Authors A Sumardin Abraham Andromeda Punjanan Agus Halid Agus Halid Agus Halid, Agus Akbar Iskandar Aman, Andryanto Andi M Yusuf Andi Maulidinnawati Abdul Kadir Parewe Andi Yulia Muniar ANGRIAWAN, RANDY Annah Annah, Annah Arif Fadillah Arif Fadillah Arif, Muhammad Iqbal Ashari Ashari Asrul Benny Leornard Encrico Panggabean Butsiarah Elita Agustina Febri Hidayat Saputra First Wanita, First Fitriana M Sabir Ghoust, Muhammad H, Muhammad Rizal ILHAM Ilham Ilham Ilham Ilham Indri Novia Putri Jumratun, Jumratun Khaidir Rahma Nasir Khaidir Rahman Natsir Latif, Nuraida Lisah Lisah M. Sabir, Fitriana Markani Markani Mashud Mashud, Mashud Maslihatin, Tatik Muhammad Arafah Muhammad Arafah Muhammad Arafah Mursalim Mursalim Mursalim Mursalim Neneng Awaliah Nyoman Suta Wijaya Pasnur Pasnur Pasnur Pasnur Pasnur Pratma, Jordan Nusti Rahmayana Rahmayana Ramlah P Ramlah P Ramlah P Randy Angriawan Ratnawati Ratnawati Ratnawati Ratnawati Resha, Muhammad Reski Praminasari Rika, Rika Suryadi Syamsu Syaharullah Disa, Syaharullah Tatik Maslihatin Tatik Maslihatin Taufik Wabdillah Wabdillah, Wabdillah Zulaikhah Fitri Nur Ngaisah