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  7. A GIS-based multi-criteria decision analysis framework for landslide risk assessment: a case study in Amathounta, Limassol, Cyprus
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A GIS-based multi-criteria decision analysis framework for landslide risk assessment: a case study in Amathounta, Limassol, Cyprus

Date Issued
September 19, 2025
Author(s)
Doukanari, Marina  
Tzouvaras, Marios  
Fotiou, Kyriaki  
Stylianou, Neophytos  
Mettas, Christodoulos  
Hadjimitsis, Diofantos G.  
DOI
10.1117/12.3075513
Abstract
Landslides pose significant risks to both infrastructure and environmental systems, making efficient risk assessment and management strategies essential. This research combines Multi-Criteria Decision Analysis (MCDA) with Geographic Information Systems (GIS) to assess landslide susceptibility in the Amathounta region of Limassol Cyprus. Nine key factors influencing slope stability were selected, including slope, aspect, relief, precipitation, land use, proximity to roads, lithology, faults, and streams—sourced from both national agencies and open datasets. A 5-meter resolution Digital Elevation Model (DEM) supported the extraction of terrain-related parameters, while geological and meteorological data were obtained from official sources. Remote sensing and spatial analysis techniques were used to prepare the input layers, and the Analytic Hierarchy Process (AHP) was employed to weight each criterion based on expert judgment and regional studies. These weighted layers were integrated using a structured overlay approach in ArcGIS Pro to generate a detailed landslide susceptibility map. The final output categorizes the study area into five hazard levels, from very low to very high risk. Validation using a local landslide inventory showed strong spatial agreement with the high-risk zones, confirming the robustness of the approach. The research provides important findings for Amathounta land-use planning and hazard mitigation and establishes a transferable method for other areas in Cyprus. The upcoming research will concentrate on expanding the model across the national territory and adding soil characteristics together with socio-economic data and real-time monitoring systems to boost predictive accuracy.
Funding(s)
ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment  
Subjects

GIS

Multi-Criteria Decisi...

Analytic Hierarchy Pr...

Landslide Risk Assess...

Predictive Modeling

Hazard Zonation

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