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The application of DInSAR and Bayesian statistics for the assessment of landslide susceptibility

Author

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  • Dimitris Kouhartsiouk

    (Geofem Ltd.)

  • Skevi Perdikou

    (Geofem Ltd.)

Abstract

The use of an inventory map of past landslide events in the derivation of susceptibility models is considered common practice. However, evidence of landslide activity may be lost due to various degrees of modification by subsequent landslides, erosional processes, vegetation growth and anthropic influences. The timely detection of active landslides can form an effective supplement to landslide records for improving the accuracy of landslide susceptibility maps. In this paper, we present a landslide susceptibility assessment carried out in a southwestern region of Cyprus using a synergy of differential interferometry and evidential statistics. A measurement of the vertical and horizontal displacements for the period 2016–2018 was done using the Small BAseline Subset multi-pass Differential Interferometric Synthetic Aperture Radar technique. Based on the results, a total of 8859 raster cells/pixels were classified as active landslides. The weight-of-evidence technique was applied to determine the weights of seven geomorphological and hydrological factors to landslide occurrence and compile a susceptibility model. The success and prediction rates of the derived model were calculated as 79.6% and 78.9%, respectively. The validation of the results against the existing landslide inventory indicates an 84% agreement with respect to the moderate and high landslide susceptibility zones. The proposed methodology can complement existing conventional landslide inventories as a means of providing updated landslide activity at frequent intervals and can provide valuable information regarding the distribution of landslides to support a detailed landslide assessment.

Suggested Citation

  • Dimitris Kouhartsiouk & Skevi Perdikou, 2021. "The application of DInSAR and Bayesian statistics for the assessment of landslide susceptibility," 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 2957-2985, February.
    • Handle: RePEc:spr:nathaz:v:105:y:2021:i:3:d:10.1007_s11069-020-04433-7
    DOI: 10.1007/s11069-020-04433-7
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    1. Aihua Wei & Kaining Yu & Fenggang Dai & Fuji Gu & Wanxi Zhang & Yu Liu, 2022. "Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    2. Xianmin Wang & Xinlong Zhang & Jia Bi & Xudong Zhang & Shiqiang Deng & Zhiwei Liu & Lizhe Wang & Haixiang Guo, 2022. "Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning," IJERPH, MDPI, vol. 19(21), pages 1-26, October.
    3. Chao Chen & Jian Zhou & Tao Zhou & Weixun Yong, 2021. "Evaluation of vertical shaft stability in underground mines: comparison of three weight methods with uncertainty theory," 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. 109(2), pages 1457-1479, November.

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