基於注意力機制的雙向長短型記憶神經網絡模型於地震預測之開發與應用

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：112

、訪客IP：3.142.171.100

姓名

方言為(Yen-Wei Fang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

基於注意力機制的雙向長短型記憶神經網絡模型於地震預測之開發與應用

相關論文

★ Nonlinear Analysis of Reinforced Concrete Structures using The Novel Implicit Nonlinear Dynamic Finite Element method	★ Abaqus軟體於3D列印混凝土分析之開發與應用
★ 數據驅動之鋼筋混凝土構架機率式地震風險評估	★ 結合深度學習與房屋街景圖像之機率式地震風險評估
★ 條件生成對抗網路於鋼筋混凝土柱遲滯迴圈預測之開發與應用	★ 鋼筋混凝土剪力牆機率式地震風險評估架構之開發與應用
★ 含物理約束之長短型記憶神經網絡模型於結構物動力反應預測之開發與應用	★ 鋼筋混凝土構架含填充磚牆機率式地震風險分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2025-8-1以後開放)

摘要(中)

臺灣位於板塊交界帶，地震發生頻繁，且常有致災型地震造成重大傷亡及經濟損失。倘若能有效地預測地震災害發生的時間、地點以及規模，將有助於減少其帶來的災損。傳統的地震預測方法通常依賴於識別地震發生的前兆或異常現象，然而此類型的方法難以進行普及化應用。近年來，數據驅動的機器學習方法在預測自然災害方面顯示出了更可靠的結果。然而，現有的研究通常需要複雜的資料處理和地震指標的選擇。為了因應這些挑戰，本研究開發了一種直接利用地震目錄進行地震預測的方法。其中透過滑動視窗法將2002年至2022年的原始歷史地震目錄處理為多元時間歷時數據，並藉由三個基於注意力機制的雙向長短型記憶神經網絡(Bidirectional Long Short-Term Memory, Bi-LSTM)模型，由先前連續發生的地震事件來預測即將發生的地震的時間、規模和位置，最後針對預測模型進行了數值實驗來優化超參數。時間預測是透過基於迴歸的學習開發，而規模和位置的預測是透過基於分類的學習來實現。與先前的模型相比，本研究提出之模型的時間預測獲得更佳的R^2，規模和位置預測得到更佳的F_1分數。儘管由於數據不平衡而容易出現過擬合，但結果顯示了使用簡單方法來加強地震預測能力的潛力。這項研究不僅推進了臺灣的地震預測，也為其他具有類似地震複雜性的地區提供了一個可延伸發揮的模型。

摘要(英)

Taiwan is situated at a tectonic boundary, making it highly prone to severe earthquakes that often result in significant loss of life and substantial economic damage. If earthquake characteristics such as the time, location, and scale of earthquake events can be effectively predicted, it will help reduce the damage caused by them. Traditional earthquake prediction methods often rely on identifying precursors or anomalies before the events. However, this type of method is difficult to universally apply. In recent years, data-driven machine-learning approaches have shown more reliable results in predicting natural disasters. However, existing studies usually require complex data processing and the selection of seismic indicators. In response to these challenges, this study introduces a novel approach that directly utilizes the earthquake catalog. By developing three attention-based Bi-LSTM models that process raw historical earthquake data from 2002 to 2022 into multivariate time series data via a sliding window technique, this research aims to predict the time, magnitude, and location of upcoming earthquakes based on previously consecutive events. Time prediction was developed through regression-based learning, while the prediction of magnitude and location was implemented through classification-based learning. Numerical experiments were conducted to optimize hyperparameters, resulting in superior R^2 of the time prediction and F_1 scores for magnitude and location over previous models. Despite some susceptibility to overfitting due to the data imbalance, the results highlight the potential of using a straightforward approach to enhance earthquake prediction capabilities. This study not only advances earthquake prediction in Taiwan but also suggests a scalable model for other regions with similar seismic complexities.

關鍵字(中)

★ 地震預測
★ 機器學習
★ 地震目錄
★ Bi-LSTM
★ 注意力機制

關鍵字(英)

★ Earthquake prediction
★ Machine learning
★ Earthquake catalog
★ Bi-LSTM
★ Attention mechanism

論文目次

目錄
一、緒論 1
1-1 研究背景與動機 1
1-2 文獻回顧 2
1-3 研究目的 7
1-4 論文架構 7
二、研究數據與數據預處理 9
2-1 地震目錄與數據來源 9
2-2 數據型態轉換 11
2-2-1 時間特徵轉換 11
2-2-2 規模特徵轉換 11
2-2-3 位置特徵轉換 11
2-3 滑動視窗法與數據分割 19
三、本研究採用之模型方法與架構介紹 21
3-1 LSTM與Bi-LSTM 21
3-2 注意力機制 23
3-3 模型架構 24
3-4 模型預測目標 25
3-4-1 各模型之預測目標種類 26
3-4-2 各模型之損失函數 27
四、數值實驗與評估方法 29
4-1 評估之性能指標 29
4-2 模型輸入數據測試 32
4-3 模型超參數測試 34
4-3-1 模型層架構 34
4-3-2 激活函數 34
4-3-3 學習率 36
4-3-4 訓練週期數 36
4-3-5 批量大小 36
4-3-6 最佳超參數結果 36
4-4 時間步長選擇 39
4-5 隨機分割數據集 41
4-6 類別權重設置 42
五、研究結果評估與分析 45
5-1 時間戳預測結果評估 45
5-2 規模預測結果評估 47
5-3 位置預測結果評估 48
5-4 與現有文獻之比較 50
5-5 亞洲區數據測試 52
六、結論與未來展望 57
6-1 結論 57
6-2 未來展望 58
參考文獻 59

參考文獻

參考文獻
[1] Geller, R. J. (1997). “Earthquake prediction: a critical review.” Geophysical Journal International, 131(3), 425–450.
[2] Wang, Q., Guo, Y., Yu, L., and Li, P. (2017). “Earthquake prediction based on spatio-temporal data mining: an lstm network approach.” IEEE Transactions on Emerging Topics in Computing, 8(1), 148–158.
[3] Al Banna, M. H., Taher, K. A., Kaiser, M. S., Mahmud, M., Rahman, M. S., Hosen, A. S., and Cho, G. H. (2020). “Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges.” IEEE Access, 8, 192880–192923.
[4] Kavianpour, P., Kavianpour, M., Jahani, E., and Ramezani, A. (2021). “Earthquake magnitude prediction using spatia-temporal features learning based on hybrid cnn-bilstm model.” 2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS), IEEE, 1–6.
[5] Mubarak, M., Riaz, M. S., Awais, M., Jilani, Z., Ahmad, N., Irfan, M., Javed, F., Alam, A., and Sultan, M. (2009). “Earthquake prediction: a global review and local research.” Proc. Pakistan Acad. Sci, 46(4), 233–246.
[6] Sikder, I. U. and Munakata, T. (2009). “Application of rough set and decision tree for characterization of premonitory factors of low seismic activity.” Expert Systems with Applications, 36(1), 102–110.
[7] Woith, H. (2015). “Radon earthquake precursor: A short review.” The European Physical Journal Special Topics, 224(4), 611–627.
[8] Alvan, H. V. and Azad, F. H. (2011). “Satellite remote sensing in earthquake prediction. a review.” 2011 National Postgraduate Conference, IEEE, 1–5.
[9] Sevgi, L. (2007). “A critical review on electromagnetic precursors and earthquake prediction.” Turkish Journal of Electrical Engineering and Computer Sciences, 15(1), 1–15.
[10] Uyeda, S., Nagao, T., and Kamogawa, M. (2009). “Short-term earthquake prediction: Current status of seismo-electromagnetics.” Tectonophysics, 470(3-4), 205–213.
[11] Huang, F., Li, M., Ma, Y., Han, Y., Tian, L., Yan, W., and Li, X. (2017). “Studies on earthquake precursors in china: A review for recent 50 years.” Geodesy and Geodynamics, 8(1), 1–12.
[12] Cicerone, R. D., Ebel, J. E., and Britton, J. (2009). “A systematic compilation of earthquake precursors.” Tectonophysics, 476(3-4), 371–396.
[13] Külahcı, F., İnceöz, M., Doğru, M., Aksoy, E., and Baykara, O. (2009). “Artificial neural network model for earthquake prediction with radon monitoring.” Applied Radiation and Isotopes, 67(1), 212–219.
[14] Cao, K. and Huang, Q. (2018). “Geo-sensor (s) for potential prediction of earthquakes: can earthquake be predicted by abnormal animal phenomena?.” Annals of GIS, 24(2), 125–138.
[15] Fidani, C. (2013). “Biological anomalies around the 2009 l’aquila earthquake.” Animals, 3(3), 693–721.
[16] Kagan, Y. Y. and Jackson, D. D. (1991). “Long-term earthquake clustering.” Geophysical Journal International, 104(1), 117–133.
[17] Kagan, Y. and Jackson, D. (1994). “Long-term probabilistic forecasting of earthquakes.” Journal of Geophysical Research: Solid Earth, 99(B7), 13685–13700.
[18] Şen, Z. (1998). “Point cumulative semivariogram for identification of heterogeneities in regional seismicity of turkey.” Mathematical Geology, 30, 767–787.
[19] Şen, Z. and Al-Suba’i, K. (2001). “Seismic hazard assessment in the tihamat asir region, south western saudi arabia.” Mathematical geology, 33, 967–991.
[20] Kannan, S. (2014). “Innovative mathematical model for earthquake prediction.” Engineering Failure Analysis, 41, 89–95.
[21] Boucouvalas, A., Gkasios, M., Tselikas, N., and Drakatos, G. (2015). “Modified-fibonacci-dual-lucas method for earthquake prediction.” Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015), Vol. 9535, SPIE, 400–410.
[22] Otari, G. and Kulkarni, R. (2012). “A review of application of data mining in earthquake prediction.” International Journal of Computer Science and Information Technologies, 3(2), 3570–3574.
[23] Azam, F., Sharif, M., Yasmin, M., and Mohsin, S. (2014). “Artificial intelligence based techniques for earthquake prediction: a review.” Sci Int, 26(4), 1495–1502.
[24] Asim, K. M., Idris, A., Iqbal, T., and Martínez-Álvarez, F. (2018). “Earthquake prediction model using support vector regressor and hybrid neural networks.” PloS one, 13(7), e0199004.
[25] Mallouhy, R., Abou Jaoude, C., Guyeux, C., and Makhoul, A. (2019). “Major earthquake event prediction using various machine learning algorithms.” 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), IEEE, 1–7.
[26] Asim, K. M., Moustafa, S. S., Niaz, I. A., Elawadi, E. A., Iqbal, T., and Martínez-Álvarez, F. (2020). “Seismicity analysis and machine learning models for short-term low magnitude seismic activity predictions in cyprus.” Soil Dynamics and Earthquake Engineering, 130, 105932.
[27] Jain, R., Nayyar, A., Arora, S., and Gupta, A. (2021). “A comprehensive analysis and prediction of earthquake magnitude based on position and depth parameters using machine and deep learning models.” Multimedia Tools and Applications, 80(18), 28419–28438.
[28] Prasad, N., Reddy, K. K., and Nirjogi, R. T. (2014). “A novel approach for seismic signal magnitude detection using haar wavelet.” 2014 5th International Conference on Intelligent Systems, Modelling and Simulation, IEEE, 324–329.
[29] Asencio-Cortés, G., Martínez-Álvarez, F., Morales-Esteban, A., and Reyes, J. (2016). “A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction.” Knowledge-Based Systems, 101, 15–30.
[30] Li, A. and Kang, L. (2009). “Knn-based modeling and its application in aftershock prediction.” 2009 International Asia Symposium on Intelligent Interaction and Affective Computing, IEEE, 83–86.
[31] Karimzadeh, S., Matsuoka, M., Kuang, J., and Ge, L. (2019). “Spatial prediction of aftershocks triggered by a major earthquake: A binary machine learning perspective.” ISPRS International Journal of Geo-Information, 8(10), 462.
[32] Li, W., Narvekar, N., Nakshatra, N., Raut, N., Sirkeci, B., and Gao, J. (2018). “Seismic data classification using machine learning.” 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), IEEE, 56–63.
[33] Zhao, G., Huang, H., and Lu, X. (2016). “Discriminating earthquakes and explosion events by seismic signals basing on bp-adaboost classifier.” 2016 2nd IEEE International Conference on Computer and Communications (ICCC), IEEE, 1965–1969.
[34] Shah, H. and Ghazali, R. (2011). “Prediction of earthquake magnitude by an improved abc-mlp.” 2011 Developments in E-systems Engineering, IEEE, 312–317.
[35] Maya, M. and Yu, W. (2019). “Short-term prediction of the earthquake through neural networks and meta-learning.” 2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), IEEE, 1–6.
[36] Panakkat, A. and Adeli, H. (2007). “Neural network models for earthquake magnitude prediction using multiple seismicity indicators.” International journal of neural systems, 17(01), 13–33.
[37] Lakshmi, S. S. and Tiwari, R. (2009). “Model dissection from earthquake time series: A comparative analysis using modern non-linear forecasting and artificial neural network approaches.” Computers & Geosciences, 35(2), 191–204.
[38] Alarifi, A. S., Alarifi, N. S., and Al-Humidan, S. (2012). “Earthquakes magnitude prediction using artificial neural network in northern red sea area.” Journal of King Saud University-Science, 24(4), 301–313.
[39] Reyes, J., Morales-Esteban, A., and Martínez-Álvarez, F. (2013). “Neural networks to predict earthquakes in chile.” Applied Soft Computing, 13(2), 1314–1328
[40] Niksarlioglu, S. and Kulahci, F. (2013). “An artificial neural network model for earthquake prediction and relations between environmental parameters and earthquakes.” International Journal of Geological and Environmental Engineering, 7(2), 87–90.
[41] Shodiq, M. N., Kusuma, D. H., Rifqi, M. G., Barakbah, A. R., and Harsono, T. (2017). “Spatial analisys of magnitude distribution for earthquake prediction using neural network based on automatic clustering in indonesia.” 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), IEEE, 246–251.
[42] Huang, J., Wang, X., Zhao, Y., Xin, C., and Xiang, H. (2018). “Large earthquake magnitude prediction in taiwan based on deep learning neural network.” Neural Network World, 28(2), 149–160.
[43] Li, R., Lu, X., Li, S., Yang, H., Qiu, J., and Zhang, L. (2020). “Dlep: A deep learning model for earthquake prediction.” 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 1–8.
[44] Shan, W., Zhang, M., Wang, M., Chen, H., Zhang, R., Yang, G., Tang, Y., Teng, Y., and Chen, J. (2022). “Epm–dcnn: Earthquake prediction models using deep convolutional neural networks.” Bulletin of the Seismological Society of America, 112(6), 2933–2945.
[45] Panakkat, A. and Adeli, H. (2009). “Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators.” Computer-Aided Civil and Infrastructure Engineering, 24(4), 280–292.
[46] Su, Z. and Zhang, Q. (2020). “Earthquake prediction based on bi-lstm+ crf model and spatio-temporal data.” 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Vol. 9, IEEE, 1190–1195.
[47] Al Banna, M. H., Ghosh, T., Al Nahian, M. J., Taher, K. A., Kaiser, M. S., Mahmud, M., Hossain, M. S., and Andersson, K. (2021). “Attention-based bi-directional long-short term memory network for earthquake prediction.” IEEE Access, 9, 56589–56603.
[48] Berhich, A., Belouadha, F.-Z., and Kabbaj, M. I. (2023). “An attention-based lstm network for large earthquake prediction.” Soil Dynamics and Earthquake Engineering, 165, 107663.
[49] Chen, Y.-I., Liu, J.-Y., Tsai, Y.-B., Chen, C.-S., et al. (2004). “Statistical tests for pre-earthquake ionospheric anomaly.” Terrestrial Atmospheric and Oceanic Sciences, 15(3), 385–396.
[50] Tsai, Y.-B., Liu, J.-Y., Shin, T.-C., Yen, H.-Y., and Chen, C.-H. (2018). “Multidisciplinary earthquake precursor studies in taiwan: a review and future prospects.” Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies, 41–65.
[51] Hu, H. and Han, Y. (2005). “Prediction of the hualian earthquakes in taiwan and an extended discussion on the method of commensurability.” Applied Geophysics, 2, 194–196.
[52] Wu, Y. M., & Chen, C. C. (2007). Seismic reversal pattern for the 1999 Chi-Chi, Taiwan, Mw 7.6 earthquake. Tectonophysics, 429(1-2), 125-132.
[53] Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE access, 8, 80716-80727.
[54] Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of Cluster in K-Means Clustering. International Journal, 1(6), 90-95.
[55] Hao, S., Lee, D. H., & Zhao, D. (2019). Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system. Transportation Research Part C: Emerging Technologies, 107, 287-300.

指導教授

陳鵬宇(Peng-Yu Chen)

審核日期

2024-8-19

推文