Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

• Published in: Land (Basel) Vol. 10; no. 9; p. 973
• Main Authors: Polykretis, Christos, Grillakis, Manolis G., Argyriou, Athanasios V., Papadopoulos, Nikos, Alexakis, Dimitrios D.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2021
• Subjects: Anthropogenic factors; Artificial lakes; Bivariate analysis; Disaster management; Disasters; Earthquakes; Forest & brush fires; Geographic information systems; Geographical Detector; Greece; hybrid modeling; information value; Landslides; Landslides & mudslides; Machine learning; Mathematical models; Modelling; Multivariate analysis; Objectivity; Risk management; Statistical analysis; Support vector machines; susceptibility; Variables; Greece; India
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land10090973

Over the last few years, landslides have occurred more and more frequently worldwide, causing severe effects on both natural and human environments. Given that landslide susceptibility (LS) assessments and mapping can spatially determine the potential for landslides in a region, it constitutes a basic step in effective risk management and disaster response. Nowadays, several LS models are available, with each one having its advantages and disadvantages. In order to enhance the benefits and overcome the weaknesses of individual modeling, the present study proposes a hybrid LS model based on the integration of two different statistical analysis models, the multivariate Geographical Detector (GeoDetector) and the bivariate information value (IV). In a GIS-based framework, the hybrid model named GeoDIV was tested to generate a reliable LS map for the vicinity of the Pinios artificial lake (Ilia, Greece), a Greek wetland. A landslide inventory of 60 past landslides and 14 conditioning (morphological, hydro-lithological and anthropogenic) factors was prepared to compose the spatial database. An LS map was derived from the GeoDIV model, presenting the different zones of potential landslides (probability) for the study area. This map was then validated by success and prediction rates—which translate to the accuracy and prediction ability of the model, respectively. The findings confirmed that hybrid modeling can outperform individual modeling, as the proposed GeoDIV model presented better validation results than the IV model.