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Comparative Study of Geological Hazard Evaluation Systems Using Grid Units and Slope Units under Different Rainfall Conditions

Author

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  • Shuai Liu

    (Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650032, China)

  • Jieyong Zhu

    (Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650032, China)

  • Dehu Yang

    (Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650032, China)

  • Bo Ma

    (Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650032, China)

Abstract

The selection of evaluation units in geological hazard evaluation systems is crucial for the evaluation results. In an evaluation system, relevant geological evaluation factors are selected and the study area is divided into multiple regular or irregular independent units, such as grids, slopes, and basins. Each evaluation unit, which includes evaluation factor attributes and hazard point distribution data, is placed as an independent individual in a corresponding evaluation model for use in a calculation, and finally a risk index for the entire study area is obtained. In order to compare the influence of the selection of grid units or slope units—two units frequently used in geological hazard evaluation studies—on the accuracy of evaluation results, this paper takes Yuanyang County, Yunnan Province, China, as a case study area. The area was divided into 7851 slope units by the catchment basin method and 12,985,257 grid units by means of an optimal grid unit algorithm. Nine evaluation factors for geological hazards were selected, including elevation, slope, aspect, curvature, land-use type, distance from a fault, distance from a river, engineering geological rock group, and landform type. In order to ensure the objective comparison of evaluation results for geological hazard susceptibility with respect to grid units and slope units, the weighted information model combining the subjective weighting AHP (analytic hierarchy process) and the objective statistical ICM (information content model) were used to evaluate susceptibility with both units. Geological risk evaluation results for collapses and landslides under heavy rain (25–50 mm), rainstorm (50–100 mm), heavy rainstorm (150–250 mm), and extraordinary rainstorm (>250 mm) conditions were obtained. The results showed that the zoning results produced under the slope unit system were better than those produced under the grid unit system in terms of the distribution relationship between hazard points and hazard levels. In addition, ROC (receiver operating characteristic) curves were used to test the results of susceptibility and risk assessments. The AUC (area under the curve) values of the slope unit system were higher than those of the grid unit system. Finally, the evaluation results obtained with slope units were more reasonable and accurate. Compared with the results from an actual geological hazard susceptibility and risk survey, the evaluation results for collapse and landslide geological hazards under the slope unit system were highly consistent with the actual survey results.

Suggested Citation

  • Shuai Liu & Jieyong Zhu & Dehu Yang & Bo Ma, 2022. "Comparative Study of Geological Hazard Evaluation Systems Using Grid Units and Slope Units under Different Rainfall Conditions," Sustainability, MDPI, vol. 14(23), pages 1-24, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16153-:d:992317
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    1. Adrián G. Bruzón & Patricia Arrogante-Funes & Fátima Arrogante-Funes & Fidel Martín-González & Carlos J. Novillo & Rubén R. Fernández & René Vázquez-Jiménez & Antonio Alarcón-Paredes & Gustavo A. Alon, 2021. "Landslide Susceptibility Assessment Using an AutoML Framework," IJERPH, MDPI, vol. 18(20), pages 1-20, October.
    2. Yigen Qin & Genlan Yang & Kunpeng Lu & Qianzheng Sun & Jin Xie & Yunwu Wu, 2021. "Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    3. Charalampos Kontoes & Constantinos Loupasakis & Ioannis Papoutsis & Stavroula Alatza & Eleftheria Poyiadji & Athanassios Ganas & Christina Psychogyiou & Mariza Kaskara & Sylvia Antoniadi & Natalia Spa, 2021. "Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WoE Methods, with Remote Sensing and Ground Truth Data," Land, MDPI, vol. 10(4), pages 1-25, April.
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    Cited by:

    1. Deborah Simon Mwakapesa & Yimin Mao & Xiaoji Lan & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance," Sustainability, MDPI, vol. 15(5), pages 1-20, February.

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