Earthquake Forecasting Using CNN-BiLSTM: Integrating Fuzzy Logic and Reinforcement Learning for Uncertainty Optimization

Mohammed Abduljaleel Shaneen and Suhad Mallalah Kadhem

References:

1. A. S. N. Alarifi, N. S. N. Alarifi, and S. Al-Humidan, “Earthquakes magnitude predication using artificial neural network in northern Red Sea area,” Journal of King Saud University - Science, vol. 24, no. 4, pp. 301-313, Oct. 2012, [Online]. Available: https://doi.org/10.1016/j.jksus.2011.05.002.
2. P. Cui et al., “The Wenchuan Earthquake (May 12, 2008), Sichuan Province, China, and resulting geohazards,” Natural Hazards, vol. 56, no. 1, pp. 19-36, Apr. 2009, [Online]. Available: https://doi.org/10.1007/s11069-009-9392-1.
3. N. Jain, D. Virmani, and A. Abraham, “Proficient 3-class classification model for confident overlap value based fuzzified aquatic information extracted tsunami prediction,” Intelligent Decision Technologies, vol. 13, no. 3, pp. 295-303, Sep. 2019, [Online]. Available: https://doi.org/10.3233/idt-180003.
4. P. Cui, L. Xiang, and Q. Zou, “Risk assessment of highways affected by debris flows in Wenchuan earthquake area,” Journal of Mountain Science, vol. 10, no. 2, pp. 173-189, Apr. 2013, [Online]. Available: https://doi.org/10.1007/s11629-013-2575-y.
5. R. Verdugo and J. González, “Liquefaction-induced ground damages during the 2010 Chile earthquake,” Soil Dynamics and Earthquake Engineering, vol. 79, pp. 280-295, Dec. 2015, [Online]. Available: https://doi.org/10.1016/j.soildyn.2015.04.016.
6. J. D. Bray, “Developing mitigation measures for the hazards associated with earthquake surface fault rupture,” University of Tokyo Press, pp. 55-79, 2001.
7. R. Bilham, “The seismic future of cities,” Bulletin of Earthquake Engineering, vol. 7, no. 4, pp. 839-887, Sep. 2009, [Online]. Available: https://doi.org/10.1007/s10518-009-9147-0.
8. N. N. Ambraseys and C. P. Melville, A History of Persian Earthquakes. Cambridge University Press, 2005.
9. J. Jia, Modern Earthquake Engineering: Offshore and Land-Based Structures. Springer, 2018.
10. G. A. Sobolev, “Methodology, results, and problems of forecasting earthquakes,” Herald of the Russian Academy of Sciences, vol. 85, no. 2, pp. 107-111, Mar. 2015, [Online]. Available: https://doi.org/10.1134/s1019331615020069.
11. R. Kail, E. Burnaev, and A. Zaytsev, “Recurrent Convolutional Neural Networks Help to Predict Location of Earthquakes,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, [Online]. Available: https://doi.org/10.1109/lgrs.2021.3107998.
12. G. V. Otari and R. V. Kulkarni, “A review of application of data mining in earthquake prediction,” International Journal of Computer Science and Information Technologies, vol. 3, no. 2, pp. 3570-3574, 2012.
13. I. M. Murwantara, P. Yugopuspito, and R. Hermawan, “Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 18, no. 3, p. 1331, Jun. 2020, [Online]. Available: https://doi.org/10.12928/telkomnika.v18i3.14756.
14. U. Khalil, B. Aslam, Z. A. Kazmi, and A. Maqsoom, “Integrated support vector regressor and hybrid neural network techniques for earthquake prediction along Chaman fault, Baluchistan,” Arabian Journal of Geosciences, vol. 14, no. 21, Oct. 2021, [Online]. Available: https://doi.org/10.1007/s12517-021-08564-4.
15. J.-W. Lin, “Researching significant earthquakes in Taiwan using two back-propagation neural network models,” Natural Hazards, Jul. 2020, [Online]. Available: https://doi.org/10.1007/s11069-020-04144-z.
16. J. Huang, X. Wang, Y. Zhao, C. Xin, and H. Xiang, “Large Earthquake Magnitude Prediction in Taiwan Based on Deep Learning Neural Network,” Neural Network World, vol. 28, no. 2, pp. 149-160, 2018, [Online]. Available: https://doi.org/10.14311/nnw.2018.28.009.
17. T. Bhandarkar, V. K, N. Satish, S. Sridhar, R. Sivakumar, and S. Ghosh, “Earthquake trend prediction using long short-term memory RNN,” International Journal of Electrical and Computer Engineering, vol. 9, no. 2, pp. 1304-1312, Apr. 2019, [Online]. Available: https://doi.org/10.11591/ijece.v9i2.pp1304-1312.
18. A. Banna, T. Ghosh, K. A. Taher, M. S. Kaiser, and M. Mahmud, “An Earthquake Prediction System for Bangladesh Using Deep Long Short-Term Memory Architecture,” Lecture Notes in Networks and Systems, pp. 465-476, 2021, [Online]. Available: https://doi.org/10.1007/978-981-33-6081-5_41.
19. R. S. Kamath and R. K. Kamat, “Earthquake magnitude prediction for Andaman-Nicobar Islands: Adaptive neuro fuzzy modeling with fuzzy subtractive clustering approach,” Journal of Chemical and Pharmaceutical Sciences, vol. 10, no. 3, pp. 1228-1233, 2017.
20. A. Mangir and V. Okumus, “Pre-earthquake fuzzy logic-based rapid hazard assessment of reinforced concrete buildings,” Case Studies in Construction Materials, vol. 19, p. e02534, Oct. 2023, [Online]. Available: https://doi.org/10.1016/j.cscm.2023.e02534.
21. S. Pradeep Kumar, L. Jasim, C. H. Basha, S. Rafikiran, and K. Parashu Ramulu, “Predicting Earthquake Damage and Rehabilitation Intervention Using Adaptive Fuzzy C-Means-Based Support Vector Machine,” in 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS), Nov. 2024, pp. 1-6, [Online]. Available: https://doi.org/10.1109/ICIICS63763.2024.108594.
22. A. V. Solomatin, “On Earthquake Prediction, the Relationship between Seismic and Geodynamic Processes, and the Concept of Information Certainty,” Journal of Volcanology and Seismology, vol. 19, no. 1, pp. 67-77, Feb. 2025, [Online]. Available: https://doi.org/10.1134/s0742046324700878.
23. W. Kuang, Z. Zou, J. Xing, and W. Wei, “Deep reinforcement learning for inverting earthquake focal mechanism and its potential application to marine earthquakes,” Intelligent Marine Technology and Systems, vol. 2, no. 1, Jun. 2024, [Online]. Available: https://doi.org/10.1007/s44295-024-00031-6.
24. W. Kuang, C. Yuan, Z. Zou, J. Zhang, and W. Zhang, “Autonomous Earthquake Location via Deep Reinforcement Learning,” Seismological Research Letters, vol. 95, no. 1, pp. 367-377, Oct. 2023, [Online]. Available: https://doi.org/10.1785/0220230118.
25. Y. Li, Y. He, and M. Zhang, “Prediction of Chinese energy structure based on Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM),” Energy Science & Engineering, 2020, [Online]. Available: https://doi.org/10.1002/ese3.698.
26. W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A CNN-LSTM-Based Model to Forecast Stock Prices,” Complexity, vol. 2020, pp. 1-10, Nov. 2020, [Online]. Available: https://doi.org/10.1155/2020/6622927.
27. A. A. Hussan, S. H. Shaker, and A. E. Ali, “Strangeness Detection from Crowded Video Scenes by Hand-Crafted and Deep Learning Features,” Journal of Soft Computing & Computer Applications, vol. 1, no. 1, Jun. 2024, [Online]. Available: https://doi.org/10.70403/3008-1084.1005.
28. S. F. Abbas, S. H. Shaker, and F. A. Abdullatif, “Face Mask Detection Based on Deep Learning: A Review,” Journal of Soft Computing and Computer Applications, vol. 1, no. 1, Art. 1006, 2024, [Online]. Available: https://doi.org/10.70403/3008-1084.1006.
29. H. K. A. Atheem, I. T. Ali, and F. A. Al Alawy, “A Comprehensive Analysis of Deep Learning and Swarm Intelligence Techniques to Enhance Vehicular Ad-hoc Network Performance,” Journal of Soft Computing and Computer Applications, vol. 1, no. 1, Art. 1004, 2024, [Online]. Available: https://doi.org/10.70403/3008-1084.1004.
30. H. Shi, K. Miao, and X. Ren, “Short-term load forecasting based on CNN-BiLSTM with Bayesian optimization and attention mechanism,” Concurrency and Computation: Practice and Experience, 2021, [Online]. Available: https://doi.org/10.1002/cpe.6676.
31. Y. Chu, X. Yue, L. Yu, M. Sergei, and Z. Wang, “Automatic Image Captioning Based on ResNet50 and LSTM with Soft Attention,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1-7, Oct. 2020, [Online]. Available: https://doi.org/10.1155/2020/8909458.
32. B. Zhang, D. Xiong, J. Xie, and J. Su, “Neural Machine Translation With GRU-Gated Attention Model,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 11, pp. 4688-4698, Jan. 2020, [Online]. Available: https://doi.org/10.1109/tnnls.2019.2957276.
33. M. H. A. Banna et al., “Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction,” IEEE Access, vol. 9, pp. 56589-56603, 2021, [Online]. Available: https://doi.org/10.1109/access.2021.3071400.
34. A. M. Andrew, “Reinforcement learning: An introduction,” Kybernetes, vol. 27, no. 9, pp. 1093-1096, Dec. 1998.
35. G. James, D. Witten, T. Hastie, and R. Tibshirani, “An introduction to statistical learning: With applications in R,” International Statistical Review, vol. 82, no. 1, pp. 156-157, Apr. 2014, [Online]. Available: https://doi.org/10.1111/insr.12051_19.