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Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model

Author

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  • Suhua Zhou

    (National Center for International Research Collaboration in Building Safety and Environment, Hunan University, Changsha 410082, China
    College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Shuaikang Zhou

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Xin Tan

    (National Center for International Research Collaboration in Building Safety and Environment, Hunan University, Changsha 410082, China
    College of Civil Engineering, Hunan University, Changsha 410082, China)

Abstract

Landslide susceptibility mapping (LSM) is a cost-effective tool for landslide hazard mitigation. To date, no nationwide landslide susceptibility maps have been produced for the entire Kenyan territory. Hence, this work aimed to develop a landslide susceptibility map at the national level in Kenya using the fuzzy analytic hierarchy process method. First, a hierarchical evaluation index system containing 10 landslide contributing factors and their subclasses was established to produce a susceptibility map. Then, the weights of these indexes were determined through pairwise comparisons, in which triangular fuzzy numbers (TFNs) were employed to scale the relative importance based on the opinions of experts. Ultimately, these weights were merged in a hierarchical order to obtain the final landslide susceptibility map. The entire Kenyan territory was divided into five susceptibility levels. Areas with very low susceptibility covered 5.53% of the Kenyan territory, areas with low susceptibility covered 20.58%, areas with the moderate susceptibility covered 29.29%, areas with high susceptibility covered 29.16%, and areas with extremely high susceptibility covered 15.44% of Kenya. The resulting map was validated using an inventory of 425 historical landslides in Kenya. The results indicated that the TFN-AHP model showed a significantly improved performance (AUC = 0.86) compared with the conventional AHP (AUC = 0.72) in LSM for the study area. In total, 31.53% and 29.88% of known landslides occurred within the “extremely high” and “high” susceptibility zones, respectively. Only 8.24% and 1.65% of known landslides fell within the “low” and “very low” susceptibility zones, respectively. The map obtained as a result of this study is beneficial to inform planning and land resource management in Kenya.

Suggested Citation

  • Suhua Zhou & Shuaikang Zhou & Xin Tan, 2020. "Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model," Land, MDPI, vol. 9(12), pages 1-22, December.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:12:p:535-:d:465814
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    References listed on IDEAS

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    1. Lucas A. Dailey & Sven Fuhrmann, 2017. "GIS-Based Logistic Regression for Landslide Susceptibility Analysis in Western Washington State," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 8(2), pages 1-19, April.
    2. Chang, Da-Yong, 1996. "Applications of the extent analysis method on fuzzy AHP," European Journal of Operational Research, Elsevier, vol. 95(3), pages 649-655, December.
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    Citations

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    Cited by:

    1. World Bank, 2022. "Understanding Compound Events in Fragile Contexts," World Bank Publications - Reports 38259, The World Bank Group.
    2. Naseem Ahmad & Muhammad Shafique & Mian Luqman Hussain & Fakhrul Islam & Aqil Tariq & Walid Soufan, 2024. "Characterization and Geomorphic Change Detection of Landslides Using UAV Multi-Temporal Imagery in the Himalayas, Pakistan," Land, MDPI, vol. 13(7), pages 1-26, June.
    3. World Bank, 2023. "Understanding Compound Events in Fragile Contexts," World Bank Publications - Reports 40749, The World Bank Group.
    4. Bo Cao & Qingyi Li & Yuhang Zhu, 2022. "Comparison of Effects between Different Weight Calculation Methods for Improving Regional Landslide Susceptibility—A Case Study from Xingshan County of China," Sustainability, MDPI, vol. 14(17), pages 1-15, September.
    5. Enrico Miccadei & Cristiano Carabella & Giorgio Paglia, 2022. "Landslide Hazard and Environment Risk Assessment," Land, MDPI, vol. 11(3), pages 1-5, March.
    6. Xiaojie Yang & Zhenli Hao & Keyuan Liu & Zhigang Tao & Guangcheng Shi, 2023. "An Improved Unascertained Measure-Set Pair Analysis Model Based on Fuzzy AHP and Entropy for Landslide Susceptibility Zonation Mapping," Sustainability, MDPI, vol. 15(7), pages 1-28, April.
    7. Farzam Tavankar & Rodolfo Picchio & Mehrdad Nikooy & Meghdad Jourgholami & Ramin Naghdi & Francesco Latterini & Rachele Venanzi, 2021. "Soil Natural Recovery Process and Fagus orientalis Lipsky Seedling Growth after Timber Extraction by Wheeled Skidder," Land, MDPI, vol. 10(2), pages 1-17, January.
    8. Lamek Nahayo & Cui Peng & Yu Lei & Rongzhi Tan, 2023. "Spatial understanding of historical and future landslide variation in Africa," 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. 119(1), pages 613-641, October.

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