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The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation

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  • Xiangyu Li

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Tianjie Lei

    (Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Jing Qin

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Jiabao Wang

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing (CUMTB), Beijing 100083, China)

  • Weiwei Wang

    (China Electronic Greatwall ShengFeiFan Information System Co., Ltd., Beijing 102200, China
    Co-firstauthors: These authors contributed equally to this work.)

  • Baoyin Liu

    (Institutes of Science and Development, University of Chinese Academy of Sciences, Beijing 100190, China)

  • Dongpan Chen

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Co-firstauthors: These authors contributed equally to this work.)

  • Guansheng Qian

    (Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    Co-firstauthors: These authors contributed equally to this work.)

  • Li Zhang

    (Beijing Institute of Technology, Beijing 100081, China
    Co-firstauthors: These authors contributed equally to this work.)

  • Jingxuan Lu

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    Co-firstauthors: These authors contributed equally to this work.)

Abstract

Most slope collapse accidents are indicated by certain signs before their occurrence, and unnecessary losses can be avoided by predicting slope deformation. However, the early warning signs of slope deformation are often misjudged. It is necessary to establish a method to determine the appropriate early warning signs in sliding thresholds. Here, to better understand the impact of different scales on the early warning signs of sliding thresholds, we used the Fisher optimal segmentation method to establish the early warning signs of a sliding threshold model based on deformation speed and deformation acceleration at different spatial scales. Our results indicated that the accuracy of the early warning signs of sliding thresholds at the surface scale was the highest. Among them, the early warning thresholds of the blue, yellow, orange, and red level on a small scale were 369.31 mm, 428.96 mm, 448.41 mm, and 923.7 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 93.25% and 92.41%, respectively. The early warning thresholds of the blue, yellow, orange, and red level on a large scale were 980.11 mm, 1038.16 mm, 2164.63 mm, and 9492.75 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 97.22% and 97.44%, respectively. Therefore, it is necessary to choose deformation at the surface scale with a large scale as the sliding threshold. Our results effectively solve the problem of misjudgment of the early warning signs of slope collapse, which is of great significance for ensuring the safe operation of water conservation projects and improving the slope deformation warning capability.

Suggested Citation

  • Xiangyu Li & Tianjie Lei & Jing Qin & Jiabao Wang & Weiwei Wang & Baoyin Liu & Dongpan Chen & Guansheng Qian & Li Zhang & Jingxuan Lu, 2023. "The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation," Land, MDPI, vol. 12(2), pages 1-14, January.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:344-:d:1048333
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    References listed on IDEAS

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    1. Dongxing Zhang & Dang Luo, 2022. "Assessment of agricultural drought loss using a skewed grey cloud ordered clustering model," 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 2787-2810, December.
    2. Aiqun Wang & Hongxiang Yu & Miaochao Chen, 2022. "The Construction and Empirical Analysis of the Company’s Financial Early Warning Model Based on Data Mining Algorithms," Journal of Mathematics, Hindawi, vol. 2022, pages 1-9, February.
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