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Earthquake disaster prediction for urban water supply systems: a review of key technologies and a multilevel assessment framework

Author

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  • Lifang Qi

    (China Earthquake Administration
    Ministry of Emergency Management)

  • Baitao Sun

    (China Earthquake Administration
    Ministry of Emergency Management)

  • Guixin Zhang

    (China Earthquake Administration
    Ministry of Emergency Management)

Abstract

Urban water supply systems are a vital part of lifeline infrastructure, and their failure after earthquakes can severely impact city operations. Earthquake disaster prediction is important, as it helps identify weak points before earthquakes and enables early prevention. However, current prediction methods lack systematization, and the variety of methods available complicates their practical application. In this study, key methods for earthquake disaster prediction in water supply systems are reviewed, their principles, characteristics, and application scenarios are analyzed, and a preliminary hierarchical and phased assessment framework is proposed. The findings support the use of scientific and practical methods for future earthquake disaster prediction efforts.

Suggested Citation

  • Lifang Qi & Baitao Sun & Guixin Zhang, 2025. "Earthquake disaster prediction for urban water supply systems: a review of key technologies and a multilevel assessment framework," 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. 121(13), pages 15109-15133, July.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:13:d:10.1007_s11069-025-07422-w
    DOI: 10.1007/s11069-025-07422-w
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    References listed on IDEAS

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    1. Alessandro Pagano & Raffaele Giordano & Ivan Portoghese, 2022. "A Pipe Ranking Method for Water Distribution Network Resilience Assessment Based on Graph-Theory Metrics Aggregated Through Bayesian Belief Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5091-5106, October.
    2. Stern, R.E. & Song, J. & Work, D.B., 2017. "Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 1-9.
    3. Jonathan Remo & Nicholas Pinter, 2012. "Hazus-MH earthquake modeling in the central USA," 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. 63(2), pages 1055-1081, September.
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