Paper Details

A methodology for landslide susceptibility assessment and mapping in a GIS environment through multicriteria decision analysis (MCDA) is presented. Landslides belong to the severe natural disasters which are oftenexperienced in Greece and have become a significant concern mainly in the mountainous areas. In this paper, theAnalytic Hierarchy Process (AHP) was applied with a varied weighted linear approach to identify the landslidepotential associated with the terrain aiming at contributing in landslide risk assessment evaluation. The study wasfocused on landslide susceptibility mapping in Atalanti catchment located in Central Greece, employing spatial analysisof factors influencing the landslide occurrence. GIS is a useful tool for the construction of landslide prediction modeland for application in regional planning, risk management and hazard mitigation as well as early warning for theprioritization of efforts to reduce future landslide hazards. In total, twelve dataset layers including slope angle, slopeaspect, rainfall, geology-lithology, curvature, elevation, land use, proximity to the rivers, faults and roads,topogragraphic wetness index (TWI) and stream power index (SPI) were selected as the causative factors for theanalysis. Digital elevation model (DEM) of 25x25m resolution was used to extract the topographic, geological,geomorphological, hydrological, land use and climatic related landslide causative-instability factors. The finalsusceptibility score was classified into susceptible rating values based on the factors’ importance and the spatiallygenerated layers were assembled to produce the final landslide indexed susceptibility assessment map. According to themap, 30.4% area of the region is moderately susceptible to the occurrence of landslides, 30.0% area is low tomoderate, 19.8% is low, 14.3% is moderate to high and only 3.1% is high to very high susceptible to the landslides’occurrences. Validation results and sensitivity analysis based on landslide inventory showed that this model could beused for the prediction of future landslides since almost 81.8% correspond to areas where landslide phenomena wereactually took place. Finally, the analysis of the susceptibility modeling showed the high importance of slope, rainfall,geology and tectonics parameters.

Lappas Ioannis, Kallioras Andreas, “Landslide susceptibility assessment and mapping using GIS and Analytic Hierarchy Process (AHP). Case study: Atalanti watershed, Central Greece”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 6, Issue 4, pp. 132-144, April 2019.