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Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)

Author

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  • Dimitrios Myronidis

    (Aristotle University of Thessaloniki)

  • Charalambos Papageorgiou

    (Aristotle University of Thessaloniki)

  • Stavros Theophanous

    (Aristotle University of Thessaloniki)

Abstract

Landslide phenomena are responsible for significant economic damages, loss of human lives and irreversible changes in natural environment. In this manuscript, in a landslide-prone site of Cyprus, a landslide susceptibility model was developed by coupling the popular analytic hierarchy process and the frequency ratio method in a GIS environment. Initially, 10 triggering factors and their modified Landslide Relative Frequencies (mLRF) were determined. Then, AHP was employed so as to determine the relative weights of each landslide instability factor and to omit those that were not essential for the model. Land cover was the most important parameter in the manifestation of landslides, while a 0.00404 consistency ratio index value revealed that the weights of the landslide contributing factors were well assessed. By combining the landslide instability agents along with their weights, a landslide susceptibility map emerged illustrating that almost 64.1 % of the study area was expanded on high-risk zones. The ROC curves and La.M.I. index statistic measures were generated by utilizing a binary file of 977,500 pixels, which had incorporated equal areas of landslide cells and randomly selected non-landslide ones. The above measures indicated that the overall model accuracy in the detection of landslide phenomena was 73.9 % and the model’s performance was enhanced when the parameters with the smallest weights were omitted from the model. This information could be significant in undertaking the appropriate series of measures according to landslide susceptibility zones in order to mitigate the landslide risk in the area.

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  • 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.
  • Handle: RePEc:spr:nathaz:v:81:y:2016:i:1:d:10.1007_s11069-015-2075-1
    DOI: 10.1007/s11069-015-2075-1
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    8. Kamila Hodasová & Martin Bednarik, 2021. "Effect of using various weighting methods in a process of landslide susceptibility assessment," 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(1), pages 481-499, January.
    9. G. Sakkas & I. Misailidis & N. Sakellariou & V. Kouskouna & G. Kaviris, 2016. "Modeling landslide susceptibility in Greece: a weighted linear combination approach using analytic hierarchical process, validated with spatial and statistical analysis," 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. 84(3), pages 1873-1904, December.
    10. Quynh Duy Bui & Hang Ha & Dong Thanh Khuc & Dinh Quoc Nguyen & Jason von Meding & Lam Phuong Nguyen & Chinh Luu, 2023. "Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam," 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 2283-2309, March.
    11. Gökhan Demir, 2018. "Landslide susceptibility mapping by using statistical analysis in the North Anatolian Fault Zone (NAFZ) on the northern part of Suşehri Town, 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. 92(1), pages 133-154, May.
    12. 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.
    13. Rui-Xuan Tang & E-Chuan Yan & Tao Wen & Xiao-Meng Yin & Wei Tang, 2021. "Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 13(7), pages 1-25, March.
    14. Konstantinos, Ioannou & Georgios, Tsantopoulos & Garyfalos, Arabatzis, 2019. "A Decision Support System methodology for selecting wind farm installation locations using AHP and TOPSIS: Case study in Eastern Macedonia and Thrace region, Greece," Energy Policy, Elsevier, vol. 132(C), pages 232-246.

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