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Atmospheric Anomalies Associated with the 2021 M w 7.2 Haiti Earthquake Using Machine Learning from Multiple Satellites

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Listed:
  • Muhammad Muzamil Khan

    (GNSS and Space Education Research Laboratory, National Center of GIS and Space Applications, Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan)

  • Bushra Ghaffar

    (Department of Environmental Science, Faculty of Basic and Applied Sciences, International Islamic University, Islamabad 44000, Pakistan)

  • Rasim Shahzad

    (GNSS and Space Education Research Laboratory, National Center of GIS and Space Applications, Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan)

  • M. Riaz Khan

    (Department of Mathematics, Quaid-i-Azam University, Islamabad 44000, Pakistan)

  • Munawar Shah

    (GNSS and Space Education Research Laboratory, National Center of GIS and Space Applications, Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan)

  • Ali H. Amin

    (Deanship of Scientific Research, Umm Al-Qura University, Makkah 24382, Saudi Arabia
    Zoology Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt)

  • Sayed M. Eldin

    (Center of Research, Faculty of Engineering, Future University in Egypt, New Cairo 11835, Egypt)

  • Najam Abbas Naqvi

    (GNSS and Space Education Research Laboratory, National Center of GIS and Space Applications, Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan)

  • Rashid Ali

    (School of Mathematics and Statistics, Central South University, Changsha 410083, China)

Abstract

The remote sensing-based Earth satellites has become a beneficial instrument for the monitoring of natural hazards. This study includes a multi-sensors analysis to estimate the spatial-temporal variations of atmospheric parameters as precursory signals to the M w 7.2 Haiti Earthquake (EQ). We studied EQ anomalies in Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), and Outgoing Longwave Radiation (OLR). Moreover, we found EQ-associated atmospheric abnormalities in a time window of 3–10 days before the main shock by different methods (e.g., statistical, wavelet transformation, deep learning, and Machine Learning (ML)-based neural networks). We observed a sharp decrease in the RH and AP before the main shock, followed by an immense enhancement in AT. Similarly, we also observed enhancement in LST and OLR around the seismic preparation region within 3–10 days before the EQ, which validates the precursory behavior of all the atmospheric parameters. These multiple-parameter irregularities can contribute with the physical understanding of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) in the future in order to forecast EQs.

Suggested Citation

  • Muhammad Muzamil Khan & Bushra Ghaffar & Rasim Shahzad & M. Riaz Khan & Munawar Shah & Ali H. Amin & Sayed M. Eldin & Najam Abbas Naqvi & Rashid Ali, 2022. "Atmospheric Anomalies Associated with the 2021 M w 7.2 Haiti Earthquake Using Machine Learning from Multiple Satellites," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14782-:d:967910
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    References listed on IDEAS

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    Cited by:

    1. Mohammad Reza Mansouri Daneshvar & Friedemann T. Freund & Majid Ebrahimi, 2023. "Spatial and Temporal Analysis of Climatic Precursors before Major Earthquakes in Iran (2011–2021)," Sustainability, MDPI, vol. 15(14), pages 1-30, July.

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