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Neural network-based methodology for inter-arrival times of earthquakes

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  • P. Kamatchi
  • K. Balaji Rao
  • Nagesh Iyer
  • S. Arunachalam

Abstract

In this paper, an artificial neural network (ANN)–based methodology is proposed to determine the probability of inter-arrival time (IAT) of main shock of six broad seismic regions of India. Initially, classical methodology using exponential distribution is applied to IAT of earthquake events computed from earthquake catalog data. From the goodness-of-fit test results, it has been found that exponential distribution is not adequate. In this paper, a more efficient ANN-based methodology is proposed, and two ANN models are developed to determine the probability of IAT of earthquake events for a specified region, specified magnitude range or magnitude greater than the specified value. The performance of ANN models developed is validated with number of examples and found to predict the probability with minimal error compared to exponential distribution model. The methodology developed can be applied to any other region with the database of the respective regions. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • P. Kamatchi & K. Balaji Rao & Nagesh Iyer & S. Arunachalam, 2012. "Neural network-based methodology for inter-arrival times of earthquakes," 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. 64(2), pages 1291-1303, November.
    • Handle: RePEc:spr:nathaz:v:64:y:2012:i:2:p:1291-1303
    DOI: 10.1007/s11069-012-0297-z
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    References listed on IDEAS

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    1. L. Arcangelis & E. Lippiello & C. Godano & M. Nicodemi, 2008. "Statistical properties and universality in earthquake and solar flare occurrence," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 64(3), pages 551-555, August.
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    Cited by:

    1. Shahram Kaboodvandpour & Jamil Amanollahi & Samira Qhavami & Bakhtiyar Mohammadi, 2015. "Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran," 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. 78(2), pages 879-893, September.
    2. Hassan Sharafi & Isa Ebtehaj & Hossein Bonakdari & Amir Hossein Zaji, 2016. "Design of a support vector machine with different kernel functions to predict scour depth around bridge piers," 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. 84(3), pages 2145-2162, December.
    3. Jamil Amanollahi & Shahram Kaboodvandpour & Hiva Majidi, 2017. "Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland, Iran," 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. 85(3), pages 1511-1527, February.

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