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Researching significant earthquakes in Taiwan using two back-propagation neural network models

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  • Jyh-Woei Lin

    (Nanjing University of Information Science and Technology)

Abstract

This study pertains to the Chi-Chi earthquake of 1999 (a Richter magnitude (ML) of 7.3), the Meishan earthquake of 1906 (a Richter magnitude (ML) of 7.1) and the Hualien earthquakes of 1951 (a Richter magnitude (ML) of 7.3), which were triggered by the Chelungpu, Meishan and Milun faults. Two back-propagation neural networks (BPNNs)—(1) an embedded earthquake Richter magnitude (ML) prediction BPNN model and (2) an active probability BPNN model—are used to predict recurrence times over 500 years. Recurrence times for a 500-year period have been studied previously. This study examines the three earthquakes again and compares the results with those for previous studies. This process does not use any probability model with exceedance probability. The Chelungpu fault and the Tamaopu-Shuangtung fault are shown to more strongly couple. This viewpoint agrees with previous studies, which suggests that the Chi-Chi earthquake was caused by the Chelungpu faults in 1999. Its recurrence time with a Richter magnitude (ML) of more than 7 is 210 years after the Chi-Chi earthquake, and the highest probability is more than 60%. The Meishan earthquake is confirmed to have been caused by the Meishan fault in 1906. There is a high probability of more than 60% of another Meishan earthquake with a Richter magnitude (ML) of more than 7 in 170 years. There is a high probability of more than 60% for the occurrence of an earthquake with a Richter magnitude (ML) of more than 7 in Hualien due to the Milun faults. The results for both BNNN models are more realistic than those of previous studies because only the earthquake catalog is used, so that the cost of study is reduced.

Suggested Citation

  • Jyh-Woei Lin, 2020. "Researching significant earthquakes in Taiwan using two back-propagation neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 3563-3590, September.
    • Handle: RePEc:spr:nathaz:v:103:y:2020:i:3:d:10.1007_s11069-020-04144-z
    DOI: 10.1007/s11069-020-04144-z
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    References listed on IDEAS

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    1. Jyh-Woei Lin, 2013. "An empirical correlation between the occurrence of earthquakes and typhoons in Taiwan: a statistical multivariate approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 605-634, January.
    2. J. Wang, 2016. "Reviews of seismicity around Taiwan: Weibull distribution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1651-1668, February.
    3. Yu Chen & Liang Chang & Chun Huang & Hone Chu, 2013. "Applying Genetic Algorithm and Neural Network to the Conjunctive Use of Surface and Subsurface Water," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4731-4757, November.
    4. J. Wang & H. Kuo-Chen, 2015. "On the use of AFOSM to estimate major earthquake probabilities in Taiwan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(3), pages 2577-2587, February.
    5. J. P. Wang, 2016. "Reviews of seismicity around Taiwan: Weibull distribution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1651-1668, February.
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