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All Journal Geographia: Jurnal Pendidikan dan Penelitian Geografi
Syamsul Arifin
Sepuluh Nopember Institute of Technology

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Earthquake clustering analysis in North Sumatra region based on double-difference relocation Rini Kumala Sari Purba; Aulia Siti Aisjah; Syamsul Arifin; Hendro Nugroho
GEOGRAPHIA: Jurnal Penelitian dan Pendidikan Geografi Vol. 6 No. 2 (2025): December backup record version
Publisher : Jurusan Pendidikan Geografi, Fakultas Ilmu Sosial dan Hukum, Universitas Negeri Manado

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Abstract

North Sumatra is an active tectonic zone influenced by the complex interaction between the subduction of the Indo-Australian plate and the movement of the Sumatra Fault. Accurate determination of earthquake hypocenter locations is crucial for seismotectonic modeling and disaster mitigation. This study aims to improve the precision of earthquake hypocenter location in this region through the implementation of the Double-Difference (HypoDD) relocation method and to analyze clustering based on the spatial distribution of earthquakes to identify active fault segments. The earthquake data used came from local seismic station catalogs during the period 2008–2024. The application of the HypoDD method significantly reduced the Root Mean Square (RMS) value of the average seismic phase arrival residual from 0.79373 to 0.33888, indicating an increase in the accuracy of the hypocenter location. Earthquake clustering analysis identified a total of 10 clusters that showed a strong correlation with the surrounding geological structure. The shallow earthquake cluster in the Tarutung region was dominated by a strike-slip fault mechanism, which definitively confirmed that the Renun Segment is an active segment of the Sumatra Fault with intense activity. In addition, a swarm pattern with low magnitude and very shallow depth (<15 km) was identified in the Toba Basin (Cluster 2), indicating the contribution of tectonic-magmatic processes. The cluster of medium- to deep-depth earthquakes (70–150 km) is strongly associated with subduction activity in the Sumatra intraslab megathrust zone. Overall, this study successfully mapped the spatial distribution pattern of earthquake sources in greater detail, contributing significantly to the updating of earthquake hazard maps and the determination of active fault zones in North Sumatra.
Daily rainfall forecast based on multi-station observation data in Medan City Nora Valencia Sinaga; Aulia Siti Aisjah; Syamsul Arifin; Immanuel Jhonson Arizona Saragih
GEOGRAPHIA: Jurnal Penelitian dan Pendidikan Geografi Vol. 6 No. 2 (2025): December backup record version
Publisher : Jurusan Pendidikan Geografi, Fakultas Ilmu Sosial dan Hukum, Universitas Negeri Manado

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This study develops a multi-horizon daily rainfall forecasting model using the Long Short-Term Memory (LSTM) deep learning method, based on multi-station Automatic Weather Station (AWS) data in Medan City. Ten-minute AWS data from multiple stations (2021–2024) were merged and time-synchronized (UTC), followed by a quality control process including physical range checks, rate-of-change filtering, inter-variable consistency checks, and spike detection. Missing values were addressed using linear interpolation for short gaps and Multiple Imputation by Chained Equations (MICE) for longer gaps. Predictor features were constructed from weather parameters (temperature, humidity, pressure, wind, radiation), aggregated to an hourly scale, and reshaped into input time windows for LSTM. A two-layer LSTM model (128–64 units, 0.3 dropout, Adam optimizer) was trained to predict daily rainfall up to five days ahead. Evaluation metrics, including RMSE, MAE, POD, FAR, and CSI (with rainfall threshold ≥1 mm/day), indicated strong model performance: for instance, RMSE was below 10 mm/day for 1–3 day horizons, with POD above 0.80 and FAR below 0.20. The LSTM model outperformed conventional statistical models, yielding an accuracy improvement of approximately 30–40%. These findings highlight the potential of high-resolution AWS-based automatic forecasting systems to support hydrometeorological disaster mitigation in tropical urban areas.
Co-Authors Aulia Siti Aisjah Hendro Nugroho Immanuel Jhonson Arizona Saragih Nora Valencia Sinaga Rini Kumala Sari Purba