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Atmospheric precursors associated with two Mw > 6.0 earthquakes using machine learning methods

Author

Listed:
  • Zaid Khalid

    (Institute of Space Technology)

  • Munawar Shah

    (Institute of Space Technology
    Tongji University)

  • Salma Riaz

    (Institute of Space Technology)

  • Bushra Ghaffar

    (International Islamic University)

  • Punyawi Jamjareegulgarn

    (King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon Campus)

Abstract

The advancements in remote sensing (RS) satellite applications have revolutionized natural disaster surveillance and prediction in the earthquake monitoring by delineating various precursors at the Earth’s surface and in atmosphere. In this paper, the earthquake precursors comprising land surface temperature, outgoing longwave radiations, relative humidity, and air temperature for both the daytime and nighttime are investigated for two Mw > 6.0 events in USA. Interestingly, we noticed surface and atmospheric parameters anomalies in 6–8 days window prior to both the events by using standard deviation method. Moreover, these abrupt deviations are also validated by the recurrent neural networks like autoregressive network with exogenous inputs and long short-term memory inputs. The findings of this study demonstrate the potential of using modern analysis tools to further develop our knowledge of the linked dynamics of the lithosphere and atmosphere preceding seismic occurrences. This study implements substantially the developing of natural hazard surveillance and earthquake prediction capabilities for future researches as a valuable addition of reference in the field of RS.

Suggested Citation

  • Zaid Khalid & Munawar Shah & Salma Riaz & Bushra Ghaffar & Punyawi Jamjareegulgarn, 2024. "Atmospheric precursors associated with two Mw > 6.0 earthquakes using machine learning methods," 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. 120(8), pages 7871-7895, June.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:8:d:10.1007_s11069-024-06562-9
    DOI: 10.1007/s11069-024-06562-9
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    References listed on IDEAS

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    1. Amna Hafeez & Muhsan Ehsan & Ayesha Abbas & Munawar Shah & Rasim Shahzad, 2022. "Machine learning-based thermal anomalies detection from MODIS LST associated with the Mw 7.7 Awaran, Pakistan earthquake," 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. 111(2), pages 2097-2115, March.
    2. Xiaoliang Xie & Bingqi Xie & Jiaqi Cheng & Qi Chu & Thomas Dooling, 2021. "A simple Monte Carlo method for estimating the chance of a cyclone impact," 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. 107(3), pages 2573-2582, July.
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