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A regional early warning model of geological hazards based on big data of real-time rainfall

Author

Listed:
  • Weidong Zhao

    (Hefei University of Technology)

  • Yunyun Cheng

    (Hefei University of Technology)

  • Jie Hou

    (Geological Environment Monitoring Station of Anhui Province)

  • Yihua Chen

    (Hefei University of Technology)

  • Bin Ji

    (Hefei University of Technology)

  • Lei Ma

    (Hefei University of Technology)

Abstract

The warning accuracy, false alarm rate and timeliness of regional geological hazard early warning models (GHEWMs) have an important impact on significantly reducing the damage caused by geological hazards. Most of the existing regional GHEWMs are based on forecast rainfall. Due to the influence of rainfall forecast accuracy and other factors, its early warning accuracy, false alarm rate and timeliness are still difficult to meet the needs of engineering applications such as disaster avoidance, mitigation and prevention of geological hazards. Therefore, this paper proposes a regional GHEWM based on the hourly rainfall series (HRS) of real-time automatic rainfall stations. Based on the data of 689 geological hazards that have occurred in Huangshan City from 2018 to 2021 and the corresponding rainfall data of automatic rainfall stations, the model uses the dynamic time warping (DTW) algorithm on the Spark big data platform to extract the historical HRS of each geological hazard and calculates the highest similarity between it and the current HRS in parallel. By coupling the probability of occurrence of geological hazards and the highest similarity of the above-mentioned HRS, a regional GHEWM based on real-time rainfall big data is finally constructed. The research results show that the model's early warning accuracy reaches 85%, and the false alarm rate is only 15%, which can predict the possibility of geological hazards after the next 3 h.

Suggested Citation

  • Weidong Zhao & Yunyun Cheng & Jie Hou & Yihua Chen & Bin Ji & Lei Ma, 2023. "A regional early warning model of geological hazards based on big data of real-time rainfall," 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. 116(3), pages 3465-3480, April.
    • Handle: RePEc:spr:nathaz:v:116:y:2023:i:3:d:10.1007_s11069-023-05819-z
    DOI: 10.1007/s11069-023-05819-z
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    1. Rattana Salee & Avirut Chinkulkijniwat & Somjai Yubonchit & Suksun Horpibulsuk & Chadanit Wangfaoklang & Sirirat Soisompong, 2022. "New threshold for landslide warning in the southern part of Thailand integrates cumulative rainfall with event rainfall depth-duration," 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. 113(1), pages 125-141, August.
    2. Derya Ozturk & Nergiz Uzel-Gunini, 2022. "Investigation of the effects of hybrid modeling approaches, factor standardization, and categorical mapping on the performance of landslide susceptibility mapping in Van, Turkey," 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. 114(3), pages 2571-2604, December.
    3. Stefano Luigi Gariano & Massimo Melillo & Silvia Peruccacci & Maria Teresa Brunetti, 2020. "How much does the rainfall temporal resolution affect rainfall thresholds for landslide triggering?," 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. 100(2), pages 655-670, January.
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