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Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, China

Author

Listed:
  • Shaohan Zhang

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China)

  • Shucheng Tan

    (School of Earth Science, Yunnan University, Kunming 650500, China)

  • Lifeng Liu

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China)

  • Duanyu Ding

    (Faculty of Architecture and City Planning, Kunming University of Science and Technology, Kunming 650500, China)

  • Yongqi Sun

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China)

  • Jun Li

    (Yunnan Architectural Engineering Design Company Limited, Kunming 650501, China)

Abstract

In China, the majority of mountainous regions are characterized by complex topography and a delicate, sensitive geological environment. These areas, which exhibit insufficient infrastructure and widespread irrational human engineering activities, are often susceptible to geological hazards such as slope instability and soil mass movements. These geological hazards pose substantial threats to human lives and property, hindering the progress of mountainous areas. Therefore, conducting research on evaluating the vulnerability of slope rock and soil mass movement geological hazards (hereinafter referred to as geological hazards) is of utmost importance for hazard prevention, emergency management, and economic advancement in these regions. This study focuses on Xuanwei City and selects eight factors for evaluation, including elevation, gradient, slope aspect, normalized vegetation index, stratigraphic lithology, distance from faults, distance from rivers, and distance from roads. These factors are chosen based on a comprehensive analysis of the spatial and temporal distribution of geological hazards and hazard incubation conditions. Two paired models, the deterministic coefficient model + logistic regression model (CF+LR) and the information quantity model + logistic regression model (I+LR), were employed to assess the study area quantitatively. The performance of these models was assessed by employing receiver operating characteristic (ROC) curves and calculating the corresponding area under curve (AUC) values. The results indicate that: (1) The AUC values for the coupled CF+LR and I+LR models are 0.799 and 0.772, respectively. These results indicate that both models provide an objective and reliable assessment of the vulnerability to geological hazards, specifically slope rock and soil mass movements, in the study area. (2) Based on the CF+LR model calculations, the geological hazard susceptibility of Xuanwei City can be categorized into four zones: extremely high susceptibility (6.09%), high susceptibility (31.08%), medium susceptibility (32.26%), and low susceptibility (30.57%). (3) The CF+LR model more accurately represents the evaluation results and offers a strong reference value.

Suggested Citation

  • Shaohan Zhang & Shucheng Tan & Lifeng Liu & Duanyu Ding & Yongqi Sun & Jun Li, 2023. "Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, Ch," Sustainability, MDPI, vol. 15(13), pages 1-19, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10466-:d:1185836
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    References listed on IDEAS

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    1. H. Pourghasemi & H. Moradi & S. Fatemi Aghda, 2013. "Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances," 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. 69(1), pages 749-779, October.
    2. Roberta Plangg Riegel & Darlan Daniel Alves & Bruna Caroline Schmidt & Guilherme Garcia Oliveira & Claus Haetinger & Daniela Montanari Migliavacca Osório & Marco Antônio Siqueira Rodrigues & Daniela M, 2020. "Assessment of susceptibility to landslides through geographic information systems and the logistic regression 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. 103(1), pages 497-511, August.
    3. Masanori Kohno & Yuki Higuchi & Yusuke Ono, 2023. "Evaluating earthquake-induced widespread slope failure hazards using an AHP-GIS combination," 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(2), pages 1485-1512, March.
    4. Xinfu Xing & Chenglong Wu & Jinhui Li & Xueyou Li & Limin Zhang & Rongjie He, 2021. "Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method," 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. 106(1), pages 97-117, March.
    5. Krishna Devkota & Amar Regmi & Hamid Pourghasemi & Kohki Yoshida & Biswajeet Pradhan & In Ryu & Megh Dhital & Omar Althuwaynee, 2013. "Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya," 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. 65(1), pages 135-165, January.
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    1. Shaohan Zhang & Shucheng Tan & Hui Geng & Ronwei Li & Yongqi Sun & Jun Li, 2023. "Evaluation of Geological Hazard Risk in Yiliang County, Yunnan Province, Using Combined Assignment Method," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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