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All Journal Jurnal Natur Indonesia Jurnal Sains & Teknologi Modifikasi Cuaca Jurnal Meteorologi Klimatologi dan Geofisika Jurnal Natural Jurnal Meteorologi dan Geofisika
Adi Mulsandi
Sekolah Tinggi Meteorologi Klimatologi Dan Geofisika
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Education Energy Environmental Science Immunology & microbiology Mathematics Neuroscience Physics

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Journal : Jurnal Natural

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A station-scale modeling framework for heavy rainfall classification in tropical weather using representative machine learning approaches MULSANDI, ADI; MIFTAHUDDIN, MIFTAHUDDIN
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v25i3.48605

Abstract

Extreme daily rainfall in rapidly urbanizing tropical cities frequently overwhelms drainage and disrupts critical services, yet station-scale forecasting remains limited by convective variability and sparse observations. This motivates lightweight, interpretable machine-learning tools that operate on routine station data. We propose and evaluate a station-scale framework to classify heavy-rainfall days (50 mm) in a humid tropical setting. Using 1,796 daily observations from the Soekarno-Hatta Meteorological Station (20182022), we engineered lag-informed predictors (e.g., previous-day rainfall, 3-day sums/means) and compared three representative classifiers, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Class imbalance was addressed with class-weighted training, and models were assessed on a held-out test set using precision, recall, F1, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC). LR achieved the highest recall (0.429), indicating moderate sensitivity to rare heavy-rainfall events, whereas RF yielded the best probabilistic discrimination (AUC = 0.619) but failed to flag positives at the default threshold; SVM displayed near-random behavior. Feature analyses highlighted humidity, temperature, and recent rainfall accumulation as the most influential predictors, consistent with tropical convective processes. Despite severe class imbalance, simple, station-based classifiers can extract actionable signals for rare-event screening in data-limited tropical regions. Operational value is likely to improve through probability calibration and threshold tuning, ensemble integration, and spatial generalization to multi-station settings.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Zakir Ahmad Muhlis Akhmad Faqih Anistia Malinda Hidayat Aries Kristianto Hidayat, Anistia Malinda Ichsan Setiawan Lutfi Fitriano Lutfi Fitriano, Lutfi Mamenun Mamenun Mamenun, Mamenun Miftahuddin Miftahuddin Munawar Munawar Munawar Munawar Perdana, Ilham Fajar Putra Prasetya, Ferdian Adhy Rahmat Hidayat Rahmat Hidayat Rismana, Yosza Indra Saputro, Hari Sopaheluwakan, Ardhasena Yonny Koesmaryono Zakir, Achmad Zakir, Achmad