Machine learning–based spatiotemporal modeling of earthquake occurrence for disaster mitigation: A case study of a seismically active region
DOI:
https://doi.org/10.58524/qammpj95Keywords:
Convolutional LSTM, Earthquake, LSTM, Mitigation Mapping, West JavaAbstract
Natural disasters, especially earthquakes, pose a significant threat that can cause significant damage to infrastructure and threaten public safety. West Java, a seismically active region, requires an effective mitigation system to minimize the impact of earthquakes. This study aims to develop an earthquake prediction model in West Java using a machine learning approach. The methods used are Long Short-Term Memory (LSTM) and Convolutional LSTM (ConvLSTM). LSTM is used to predict earthquake magnitude and depth and also prediction for 2025 - 2029, while ConvLSTM is used to predict the location of the earthquake epicenter. Historical earthquake data including location, magnitude, and depth are used to train the model. The results of the study showed that the ConvLSTM model was able to predict 104 earthquake events over the next five years with an RMSE of 7.907, MAPE of 32.65%, and of 0.245, while LSTM for magnitude produced an RMSE of 0.413, MAPE of 7.00%, and of 0.457, resulting in predictions with a magnitude range of 4.10 to 5.03 for all earthquake events for the next 5 years. Depth prediction with LSTM also showed significant results with an RMSE of 1.464, MAPE of 45.50%, and of 0.399, where most earthquakes were predicted to occur in Zone 2 and Zone 3, indicating that the depth of the earthquake tended to be shallow to medium in the next 5 years. These results can be used to increase public awareness and regional resilience through mapping earthquake-prone areas, as well as planning infrastructure strengthening and developing early warning systems.
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Acuña Espinoza, E., Kratzert, F., Klotz, D., Gauch, M., Álvarez Chaves, M., Loritz, R., & Ehret, U. (2025). Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell. Hydrology and Earth System Sciences, 29(6), 1749–1758. https://doi.org/10.5194/hess-29-1749-2025
Anderson, S., & Radić, V. (2022). Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling. Hydrology and Earth System Sciences, 26(3), 795–825. https://doi.org/10.5194/hess-26-795-2022
Anshuka, A., Chandra, R., Buzacott, A. J. V., Sanderson, D., & van Ogtrop, F. F. (2022). Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model. Stochastic Environmental Research and Risk Assessment, 36(10), 3467–3485. https://doi.org/10.1007/s00477-022-02204-3
Ariyaningsih, A., Erik, B., & Sukmara, B. (2021). Kriteria ketahanan kota berdasarkan jenis bencana prioritas di Kota Balikpapan. Region : Jurnal Pembangunan Wilayah Dan Perencanaan Partisipatif, 16(1), 74–82. https://doi.org/10.20961/region.v16i1.44149
Aryal, M., Tiwari, R. K., & Paudyal, H. (2024). Prediction of earthquakes in Nepal and the adjoining regions using LSTM. BMC Journal of Scientific Research, 7(1), 12–26. https://doi.org/10.3126/bmcjsr.v7i1.73058
Baste, S., Klotz, D., Acuña Espinoza, E., Bardossy, A., & Loritz, R. (2025). Unveiling the limits of deep learning models in hydrological extrapolation tasks. Hydrology and Earth System Sciences, 29(21), 5871–5891. https://doi.org/10.5194/hess-29-5871-2025
Gallardo, J. N., Gloria, E. D. G., Landicho, N. R. P., & Sueno, H. T. (2023). Detection of hate speech using improved deep learning techniques. 2023 10th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 184–189. https://doi.org/10.1109/ICITACEE58587.2023.10277103
Geiß, C., Maier, J., So, E., Schoepfer, E., Harig, S., Gómez Zapata, J. C., & Zhu, Y. (2024). Anticipating a risky future: Long short-term memory (LSTM) models for spatiotemporal extrapolation of population data in areas prone to earthquakes and tsunamis in Lima, Peru. Natural Hazards and Earth System Sciences, 24(3), 1051–1064. https://doi.org/10.5194/nhess-24-1051-2024
Hartono, H., Anwar, H., Umam, R., & Takahashi, H. (2024). An unsupervised machine learning algorithm approach using K-means clustering for optimizing surface wave filtering in seismic reflection data. Journal of Natural Sciences and Mathematics Research, 10(1), 114–127.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kader, N. I. A., Yusof, U. K., Khalid, M. N. A., & Husain, N. R. N. (2023). A review of long short-term memory approach for time series analysis and forecasting. In M. A. Al-Sharafi, M. Al-Emran, M. N. Al-Kabi, & K. Shaalan (Eds.), Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems (pp. 12–21). Springer International Publishing. https://doi.org/10.1007/978-3-031-20429-6_2
Kumar, A., Islam, T., Sekimoto, Y., Mattmann, C., & Wilson, B. (2020). Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data. PLOS ONE, 15(3), e0230114. https://doi.org/10.1371/journal.pone.0230114
Kuo, P.-H., Chen, H.-C., & Huang, C.-J. (2018). Solar daily irradiation estimation algorithm development and field verification. 2018 IEEE International Conference on Applied System Invention (ICASI), 166–167. https://doi.org/10.1109/ICASI.2018.8394558
Landi, F., Baraldi, L., Cornia, M., & Cucchiara, R. (2021). Working memory connections for LSTM. Neural Networks, 144, 334–341. https://doi.org/10.1016/j.neunet.2021.08.030
Lou, W., Gao, X., Lee, J. H. W., Liu, J., Peng, J., Dong, L., & Gao, K. (2025). A highly generalizable data-driven model for spatiotemporal urban flood dynamics real-time forecasting based on coupled CNN and ConvLSTM. EGUsphere, 1–24. https://doi.org/10.5194/egusphere-2025-3171
Munandar, R. A., & Sulsaladin, R. (2022). Karakteristik tektonik dan periode ulang gempabumi pada sesar Cimandiri Jawa Barat. Buletin Meteorologi, Klimatologi Dan Geofisika, 2(4), 42–51. https://www.balai2bmkg.id/index.php/buletin_mkg/article/view/30
Pakpahan, S., Tambunan, M. P., Mannesa, M. D. M., & Tambunan, R. P. (2021). Pola spasial bahaya gempa bumi di sekitar bandara Kertajati dan kesesuaiannya terhadap tata ruang wilayah. Jurnal Geosaintek, 7(2), 73–82. https://doi.org/10.12962/j25023659.v7i2.8590
Permana, M. A., & Faisal, M. (2023). Uji performa prediksi gempa bumi di Jawa Timur dengan artificial neural network. Euler : Jurnal Ilmiah Matematika, Sains Dan Teknologi, 11(1), 44–54. https://doi.org/10.34312/euler.v11i1.19291
Prastowo, R., Ipmawan, V. L., Zamroni, A., Umam, R., Permanasari, I. N. P., & Siregar, R. N. (2020). Identification of ground motion prone areas triggering earthquakes based on microtremor data in Jati Agung district, South Lampung Regency, Lampung, Indonesia. AIP Conference Proceedings, 2245(1), 070003. https://doi.org/10.1063/5.0012081
Saputro, I. T., & Momot, H. (2020). Pemutakhiran peta sebaran gempa bumi berdasarkan magnitudo dan kedalaman di wilayah provinsi Papua Barat pada 50 tahun terakhir. TAPAK (Teknologi Aplikasi Konstruksi) : Jurnal Program Studi Teknik Sipil, 10(1), 1–9. https://doi.org/10.24127/tp.v10i1.1315
Sattari, A., Foroumandi, E., Gavahi, K., & Moradkhani, H. (2025). A probabilistic machine learning framework for daily extreme events forecasting. Expert Systems with Applications, 265, 126004. https://doi.org/10.1016/j.eswa.2024.126004
Scheingraber, C., & Käser, M. (2020). Spatial seismic hazard variation and adaptive sampling of portfolio location uncertainty in probabilistic seismic risk analysis. Natural Hazards and Earth System Sciences, 20(7), 1903–1918. https://doi.org/10.5194/nhess-20-1903-2020
Tehseen, R., Farooq, M. S., & Abid, A. (2020). Earthquake prediction using expert systems: A systematic mapping study. Sustainability, 12(6), 2420. https://doi.org/10.3390/su12062420
Varshney, R. P., & Sharma, D. K. (2024). Optimizing time-series forecasting using stacked deep learning framework with enhanced adaptive moment estimation and error correction. Expert Systems with Applications, 249, 123487. https://doi.org/10.1016/j.eswa.2024.123487
Wang, Q., Peng, R.-Q., Wang, J.-Q., Li, Z., & Qu, H.-B. (2020). NEWLSTM: An optimized long short-term memory language model for sequence prediction. IEEE Access, 8, 65395–65401. https://doi.org/10.1109/ACCESS.2020.2985418
Yang, E., Zhang, H., Guo, X., Zang, Z., Liu, Z., & Liu, Y. (2022). A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China. BMC Infectious Diseases, 22(1), 490. https://doi.org/10.1186/s12879-022-07462-8
Yao, S., He, Y., Zhang, L., Yang, W., Chen, Y., Sun, Q., Zhao, Z., & Cao, S. (2023). A convLSTM neural network model for spatiotemporal prediction of mining area surface deformation based on SBAS-InSAR monitoring data. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–22. https://doi.org/10.1109/TGRS.2023.3236510
Zhang, H., Ke, S., Liu, W., & Zhang, Y. (2024). A combining earthquake forecasting model between deep learning and epidemic-type aftershock sequence (ETAS) model. Geophysical Journal International, 239(3), 1545–1556. https://doi.org/10.1093/gji/ggae349
Zhang, Z., & Wang, Y. (2023a). A spatiotemporal model for global earthquake prediction based on convolutional LSTM. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–12. https://doi.org/10.1109/TGRS.2023.3302316
Zhang, Z., & Wang, Y. (2023b). A spatiotemporal model for global earthquake prediction based on convolutional LSTM. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2023.3302316
Zhang, Z., & Wang, Y. (2024). A global earthquake prediction model based on spherical convolutional LSTM. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–10. https://doi.org/10.1109/TGRS.2024.3380573
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Copyright (c) 2026 Muhammad Harun Yahya, Muhamad Syazali, Arifan Jaya Syahbana, Rofiqul Umam, Hirotaka Takahashi (Author)
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