Probabilistic forecasting of m≥5.0 earthquakes in East Java: A 30-day LSTM approach using seismic feature data

Yulianto, Nanang, Chamidy, Totok, Imamudin, Mochamad ORCID: https://orcid.org/0009-0006-7522-3710, Suhartono, Suhartono ORCID: https://orcid.org/0000-0002-4304-4279 and Yaqin, Muhammad Ainul ORCID: https://orcid.org/0000-0002-6541-0911 (2026) Probabilistic forecasting of m≥5.0 earthquakes in East Java: A 30-day LSTM approach using seismic feature data. G-Tech : Jurnal Teknologi Terapan, 10 (2). pp. 866-876. ISSN 2580-8737

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Abstract

East Java is a seismically active region where short-term earthquake forecasting remains a critical yet challenging endeavor. While deterministic prediction is inherently unfeasible, probabilistic modeling offers a practical pathway for risk mitigation. This study develops a 30-day forward-window probabilistic forecasting model for M≥5.0 earthquakes in East Java using a Long Short-Term Memory (LSTM) network framed as a binary classification task. The model is trained on 25 years of seismic data (2001–2025) from BMKG Stasiun Geofisika Pasuruan. Twenty-five seismic features were rigorously selected through correlation analysis and data-leakage prevention protocols, while class imbalance was mitigated using adaptive loss weighting. The LSTM architecture was systematically optimized via sequential hyperparameter tuning and robust validation strategies. On a hold-out test set, the model achieved an AUC-ROC of 0.752, F1-score of 0.484, and recall of 0.673, indicating the model's capacity to detect impending seismic events with reasonable sensitivity. These results confirm that deep learning can effectively capture non-linear temporal patterns in seismic sequences. The primary contribution of this work is a validated, operationally ready probabilistic forecasting framework that can be integrated into regional earthquake monitoring systems, providing actionable lead time for disaster preparedness in East Java.

Item Type:	Journal Article
Keywords:	earthquake prediction; long short-term memory; probabilistic forecasting; binary classification; seismicity parameters
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	Mochamad Imamudin
Date Deposited:	13 Jul 2026 09:41

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