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Modeling landslide susceptibility in Greece: a weighted linear combination approach using analytic hierarchical process, validated with spatial and statistical analysis

Author

Listed:
  • G. Sakkas

    (National and Kapodistrian University of Athens)

  • I. Misailidis

    (National and Kapodistrian University of Athens)

  • N. Sakellariou

    (National and Kapodistrian University of Athens)

  • V. Kouskouna

    (National and Kapodistrian University of Athens)

  • G. Kaviris

    (National and Kapodistrian University of Athens)

Abstract

A new model for Greece depicting, at a national scale, the spatial distribution of areas prone to landslide phenomena, taking into account preparatory and triggering criteria, is presented. This is a baseline study for prioritizing research in hazardous areas with more detail. Landslide occurrence is very high in certain areas of the Greek territory, such as Western Greece. Analysis is performed by applying two models, both based on slopes and geotechnical data, earthquake occurrence and precipitation. The first, which yields better results, also takes into account land use data. Since there was no access to landslide inventories, landslide susceptibility was performed on empirical estimates of the aforementioned criteria, using the analytic hierarchy process in order to derive the proper weights for each criterion. The final outcome is calculated based on the weighted linear combination of the above-mentioned criteria. The present study covers the area of Greece with an accuracy of 500 m × 500 m grid cell size. Results have been validated with observed events, landsliding urban areas and other studies, thus identifying landslide prone areas in a satisfying manner.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:nathaz:v:84:y:2016:i:3:d:10.1007_s11069-016-2523-6
    DOI: 10.1007/s11069-016-2523-6
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    3. Christos Polykretis & Christos Chalkias, 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models," 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. 93(1), pages 249-274, August.
    4. Luliang Liu & Yuanming Dou & Jiangang Qiao, 2022. "Evaluation Method of Highway Plant Slope Based on Rough Set Theory and Analytic Hierarchy Process: A Case Study in Taihang Mountain, Hebei, China," Mathematics, MDPI, vol. 10(8), pages 1-17, April.

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