GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece

Christos Chalkias,Christos Polykretis,Maria Ferentinou

doi:10.3390/geosciences4030176

Christos Chalkias, Christos Polykretis + Show 1 more

Open Access

https://doi.org/10.3390/geosciences4030176

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Abstract

: In this paper, bivariate statistical analysis modeling was applied and validated to derive a landslide susceptibility map of Peloponnese (Greece) at a regional scale. For this purpose, landslide-conditioning factors such as elevation, slope, aspect, lithology, land cover, mean annual precipitation (MAP) and peak ground acceleration (PGA), and a landslide inventory were analyzed within a GIS environment. A landslide dataset was realized using two main landslide inventories. The landslide statistical index method (LSI) produced a susceptibility map of the study area and the probability level of landslide occurrence was classified in five categories according to the best classification method from three different methods tested. Model performance was checked by an independent validation set of landslide events. The accuracy of the final result was evaluated by receiver operating characteristics (ROC) analysis. The prediction ability was found to be 75.2% indicating an acceptable susceptibility map obtained from the GIS-based bivariate statistical model.

Highlights

Landslides are one of the major types of geo-hazards [1] as almost 9% of global natural disasters refer to landslides [2]
For a conditioning factor to be useful for landslide susceptibility mapping, its categories should provide a range of landslide susceptibility index (LSI) values
The results of the statistical (LSI) analysis are summarized in Tables 1 and 2

Summary

Introduction

Landslides are one of the major types of geo-hazards [1] as almost 9% of global natural disasters refer to landslides [2]. Landslide susceptibility (LS) is the propensity of soil or rock to produce various types of landslides [3,4]. A LS map presents areas with the potential for landsliding in the future by combining some of the critical factors that contributed to the occurrence of past landslides [5]. Such a map is a valuable tool for assessing current and potential risks that can be used for developing early warning systems and mitigation plans, such as selecting the most suitable locations for construction of structures and roads

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