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Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Author

Listed:
  • Qianqian Wang

    (School of Geology and Geomatics, Tianjin Chengjian University, No. 26 Jinjing RD., Xiqing District, Tianjin 300384, China)

  • Dongchuan Wang

    (School of Geology and Geomatics, Tianjin Chengjian University, No. 26 Jinjing RD., Xiqing District, Tianjin 300384, China)

  • Yong Huang

    (Appraisal Center for Environment & Engineering Ministry of Environmental Protection, No. 8 Beiyuan RD., Chaoyang District, Beijing 100012, China)

  • Zhiheng Wang

    (School of Geology and Geomatics, Tianjin Chengjian University, No. 26 Jinjing RD., Xiqing District, Tianjin 300384, China)

  • Lihui Zhang

    (School of Geology and Geomatics, Tianjin Chengjian University, No. 26 Jinjing RD., Xiqing District, Tianjin 300384, China)

  • Qiaozhen Guo

    (School of Geology and Geomatics, Tianjin Chengjian University, No. 26 Jinjing RD., Xiqing District, Tianjin 300384, China)

  • Wei Chen

    (School of Geology and Geomatics, Tianjin Chengjian University, No. 26 Jinjing RD., Xiqing District, Tianjin 300384, China)

  • Wengang Chen

    (School of Geology and Geomatics, Tianjin Chengjian University, No. 26 Jinjing RD., Xiqing District, Tianjin 300384, China)

  • Mengqin Sang

    (School of Geology and Geomatics, Tianjin Chengjian University, No. 26 Jinjing RD., Xiqing District, Tianjin 300384, China)

Abstract

Landslides are usually initiated under complex geological conditions. It is of great significance to find out the optimal combination of predisposing factors and create an accurate landslide susceptibility map based on them. In this paper, the Information Value Model was modified to make the Modified Information Value (MIV) Model, and together with GIS (Geographical Information System) and AUC (Area Under Receiver Operating Characteristic Curve) test, 32 factor combinations were evaluated separately, and factor combination group with members Slope, Lithology, Drainage network, Annual precipitation, Faults, Road and Vegetation was selected as the optimal combination group with an accuracy of 95.0%. Based on this group, a landslide susceptibility zonation map was drawn, where the study area was reclassified into five classes, presenting an accurate description of different levels of landslide susceptibility, with 79.41% and 13.67% of the validating field survey landslides falling in the Very High and High zones, respectively, mainly distributed in the south and southeast of the catchment. It showed that MIV model can tackle the problem of “no data in subclass” well, generate the true information value and show real running trend, which performs well in showing the relationship between predisposing factors and landslide occurrence and can be used for preliminary landslide susceptibility assessment in the study area.

Suggested Citation

  • Qianqian Wang & Dongchuan Wang & Yong Huang & Zhiheng Wang & Lihui Zhang & Qiaozhen Guo & Wei Chen & Wengang Chen & Mengqin Sang, 2015. "Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
  • Handle: RePEc:gam:jsusta:v:7:y:2015:i:12:p:15839-16669:d:60761
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    2. Langping Li & Hengxing Lan, 2020. "Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study," IJERPH, MDPI, vol. 17(21), pages 1-17, November.
    3. Sangseom Jeong & Azman Kassim & Moonhyun Hong & Nader Saadatkhah, 2018. "Susceptibility Assessments of Landslides in Hulu Kelang Area Using a Geographic Information System-Based Prediction Model," Sustainability, MDPI, vol. 10(8), pages 1-19, August.
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    5. Jamal Jokar Arsanjani & Eric Vaz, 2017. "Special Issue Editorial: Earth Observation and Geoinformation Technologies for Sustainable Development," Sustainability, MDPI, vol. 9(5), pages 1-5, May.
    6. Suhua Zhou & Guangqi Chen & Ligang Fang & Yunwen Nie, 2016. "GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping," Sustainability, MDPI, vol. 8(4), pages 1-15, April.
    7. Halil Akinci & Mustafa Zeybek, 2021. "Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), 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. 108(2), pages 1515-1543, September.
    8. 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.

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