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

Christos Polykretis, Manolis G. Grillakis, Athanasios V. Argyriou, Nikos Papadopoulos and Dimitrios D. Alexakis
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Christos Polykretis: Laboratory of Geophysical—Satellite Remote Sensing and Archaeo-Environment (GeoSat ReSeArch Lab), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology—Hellas (FORTH), 74100 Rethymno, Greece
Manolis G. Grillakis: Laboratory of Geophysical—Satellite Remote Sensing and Archaeo-Environment (GeoSat ReSeArch Lab), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology—Hellas (FORTH), 74100 Rethymno, Greece
Athanasios V. Argyriou: Laboratory of Geophysical—Satellite Remote Sensing and Archaeo-Environment (GeoSat ReSeArch Lab), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology—Hellas (FORTH), 74100 Rethymno, Greece
Nikos Papadopoulos: Laboratory of Geophysical—Satellite Remote Sensing and Archaeo-Environment (GeoSat ReSeArch Lab), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology—Hellas (FORTH), 74100 Rethymno, Greece
Dimitrios D. Alexakis: Laboratory of Geophysical—Satellite Remote Sensing and Archaeo-Environment (GeoSat ReSeArch Lab), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology—Hellas (FORTH), 74100 Rethymno, Greece

Land, 2021, vol. 10, issue 9, 1-23

Abstract: 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.

Keywords: landslides; susceptibility; hybrid modeling; Geographical Detector; information value; Greece (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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