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 and Yaqin, Muhammad Ainul (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 2623-064X

Preview

Text 4.+Nanang+Fix.pdf Download (567kB) | Preview

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 > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Totok Chamidy
Date Deposited:	10 Jul 2026 15:28

Downloads

Origin of downloads

Actions (login required)

View Item