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Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada

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  • Zhaohua Chen
  • Jinfei Wang

Abstract

A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. A case study is conducted in the mountainous southern Mackenzie Valley, Northwest Territories, Canada. To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. In this study, bedrock, surface materials, slope, and difference between surface aspect and dip direction of the sedimentary rock were found to be the most important factors affecting landslide occurrence. The influence on landslides by interactions among geologic and geomorphic conditions is also analyzed, and used to develop a logistic regression model for landslide hazard mapping. The comparison of the results from the model including the interaction terms and the model not including the interaction terms indicate that interactions among the variables were found to be significant for predicting future landslide probability and locating high hazard areas. The results from this study demonstrate that the use of a logistic regression model within a GIS framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie Valley. Copyright Springer Science+Business Media, Inc. 2007

Suggested Citation

  • Zhaohua Chen & Jinfei Wang, 2007. "Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada," 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. 42(1), pages 75-89, July.
    • Handle: RePEc:spr:nathaz:v:42:y:2007:i:1:p:75-89
    DOI: 10.1007/s11069-006-9061-6
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    Citations

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    Cited by:

    1. Yongchao Li & Jianping Chen & Chun Tan & Yang Li & Feifan Gu & Yiwei Zhang & Qaiser Mehmood, 2021. "Application of the borderline-SMOTE method in susceptibility assessments of debris flows in Pinggu District, Beijing, China," 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. 105(3), pages 2499-2522, February.
    2. Dimitrios Myronidis & Charalambos Papageorgiou & Stavros Theophanous, 2016. "Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)," 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. 81(1), pages 245-263, March.
    3. Hua Wang & Qing Guo & Xiaoqing Ge & Lianzi Tong, 2022. "A Spatio-Temporal Monitoring Method Based on Multi-Source Remote Sensing Data Applied to the Case of the Temi Landslide," Land, MDPI, vol. 11(8), pages 1-19, August.
    4. Yumiao Wang & Xueling Wu & Zhangjian Chen & Fu Ren & Luwei Feng & Qingyun Du, 2019. "Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China," IJERPH, MDPI, vol. 16(3), pages 1-27, January.
    5. Gao Hua-xi & Yin Kun-long, 2014. "Study on spatial prediction and time forecast of landslide," 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. 70(3), pages 1735-1748, February.
    6. Jie Dou & Ali P. Yunus & Yueren Xu & Zhongfan Zhu & Chi-Wen Chen & Mehebub Sahana & Khabat Khosravi & Yong Yang & Binh Thai Pham, 2019. "Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China," 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. 97(2), pages 579-609, June.
    7. Dimitrios Myronidis & Charalambos Papageorgiou & Stavros Theophanous, 2016. "Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)," 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. 81(1), pages 245-263, March.
    8. Cahio Guimarães Seabra Eiras & Juliana Ribeiro Gonçalves de Souza & Renata Delicio Andrade de Freitas & César Falcão Barella & Tiago Martins Pereira, 2021. "Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data," 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. 107(2), pages 1427-1442, June.
    9. Netra Bhandary & Ranjan Dahal & Manita Timilsina & Ryuichi Yatabe, 2013. "Rainfall event-based landslide susceptibility zonation mapping," 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 365-388, October.
    10. Laura Turconi & Fabio Luino & Mattia Gussoni & Francesco Faccini & Marco Giardino & Marco Casazza, 2019. "Intrinsic Environmental Vulnerability as Shallow Landslide Susceptibility in Environmental Impact Assessment," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    11. Zhiheng Wang & Dongchuan Wang & Qiaozhen Guo & Daikun Wang, 2020. "Regional landslide hazard assessment through integrating susceptibility index and rainfall process," 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. 104(3), pages 2153-2173, December.

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