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Earthquake prediction model using support vector regressor and hybrid neural networks

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  • Khawaja M Asim
  • Adnan Idris
  • Talat Iqbal
  • Francisco Martínez-Álvarez

Abstract

Earthquake prediction has been a challenging research area, where a future occurrence of the devastating catastrophe is predicted. In this work, sixty seismic features are computed through employing seismological concepts, such as Gutenberg-Richter law, seismic rate changes, foreshock frequency, seismic energy release, total recurrence time. Further, Maximum Relevance and Minimum Redundancy (mRMR) criteria is applied to extract the relevant features. A Support Vector Regressor (SVR) and Hybrid Neural Network (HNN) based classification system is built to obtain the earthquake predictions. HNN is a step wise combination of three different Neural Networks, supported by Enhanced Particle Swarm Optimization (EPSO), to offer weight optimization at each layer. The newly computed seismic features in combination with SVR-HNN prediction system is applied on Hindukush, Chile and Southern California regions. The obtained numerical results show improved prediction performance for all the considered regions, compared to previous prediction studies.

Suggested Citation

  • Khawaja M Asim & Adnan Idris & Talat Iqbal & Francisco Martínez-Álvarez, 2018. "Earthquake prediction model using support vector regressor and hybrid neural networks," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-22, July.
    • Handle: RePEc:plo:pone00:0199004
    DOI: 10.1371/journal.pone.0199004
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    Cited by:

    1. KASHIWAGI Yuzuka & TODO Yasuyuki, 2021. "How Do Disasters Change Inter-Group Perceptions? Evidence from the 2018 Sulawesi Earthquake," Discussion papers 21082, Research Institute of Economy, Trade and Industry (RIETI).
    2. Suzlyana Marhain & Ali Najah Ahmed & Muhammad Ary Murti & Pavitra Kumar & Ahmed El-Shafie, 2021. "Investigating the application of artificial intelligence for earthquake prediction in Terengganu," 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. 108(1), pages 977-999, August.
    3. Rabia Tehseen & Muhammad Shoaib Farooq & Adnan Abid, 2020. "Earthquake Prediction Using Expert Systems: A Systematic Mapping Study," Sustainability, MDPI, vol. 12(6), pages 1-32, March.

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