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Landslide susceptibility, Peloponnese Peninsula in South Greece

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  • C. Chalkias
  • S. Kalogirou
  • M. Ferentinou

Abstract

The aim of this paper is to investigate landslide susceptibility mapping in regional scale, considering the spatial stationarity of the relationship between landslide susceptibility and its influencing factors. Landslides are among the most severe natural hazards and their management has a key role to human safety. During the last decades, a significant number of GIS-based methods for landslide susceptibility assessment and mapping have been proposed in the literature. In this paper, contemporary methods for landslide susceptibility analysis have been applied. The latter include global and local regression analysis aiming to study the relationship between landslide occurrence and its determinants. This paper also examines if this relationship is spatial non-stationary via the application of the Geographically Weighted Regression (GWR). The proposed methodology has been applied in the Peloponnese peninsula, in South Greece. To examine the factors responsible for the occurrence of a landslide event; topographic (slope angle, elevation), geological and other environmental variables (land cover, rainfalls) were considered. The results suggest that GWR provides a potential improvement in landslide susceptibility assessment compared to traditional global regression analysis methods.

Suggested Citation

  • C. Chalkias & S. Kalogirou & M. Ferentinou, 2014. "Landslide susceptibility, Peloponnese Peninsula in South Greece," Journal of Maps, Taylor & Francis Journals, vol. 10(2), pages 211-222, April.
    • Handle: RePEc:taf:tjomxx:v:10:y:2014:i:2:p:211-222
    DOI: 10.1080/17445647.2014.884022
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    Cited by:

    1. Xianyu Yu & Yi Wang & Ruiqing Niu & Youjian Hu, 2016. "A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, Chin," IJERPH, MDPI, vol. 13(5), pages 1-35, May.
    2. 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.
    3. Emmanouil Psomiadis & Andreas Papazachariou & Konstantinos X. Soulis & Despoina-Simoni Alexiou & Ioannis Charalampopoulos, 2020. "Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data," Land, MDPI, vol. 9(5), pages 1-26, April.
    4. Bin Zhao & Xuexi Yang & Qianhong Wu & Weifeng Xiao & Wentao Yang & Min Deng, 2022. "Uncovering the Structural Effect Mechanisms of Natural and Social Factors on Land Subsidence: A Case Study in Beijing," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    5. Shengwu Qin & Shuangshuang Qiao & Jingyu Yao & Lingshuai Zhang & Xiaowei Liu & Xu Guo & Yang Chen & Jingbo Sun, 2022. "Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale," 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 2709-2738, December.
    6. Guilherme Garcia Oliveira & Luis Fernando Chimelo Ruiz & Laurindo Antonio Guasselli & Claus Haetinger, 2019. "Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil," 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. 99(2), pages 1049-1073, November.

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