A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping

By Bakhtiar Feizizadeh, Majid Shadman Roodposhti, Piotr L. Jankowski, Thomas Blaschke. Computational Geosciences, 2014

344 citations recorded by the source corpus.

Landslide susceptibility mapping (LSM) is making increasing use of GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. We have developed a new multi-criteria decision analysis (MCDA) method for LSM and applied it to the Izeh River basin in south-western Iran. Our method is based on fuzzy membership functions (FMFs) derived from GIS analysis. It makes use of nine causal landslide factors identified by local landslide experts. Fuzzy set theory was first integrated with an analytical hierarchy process (AHP) in order to use pairwise comparisons to compare LSM criteria for ranking purposes. FMFs were then applied in order to determine the criteria weights to be used in the development of a landslide susceptibility map. Finally, a landslide inventory database was used to validate the LSM map by comparing it with known landslides within the study area. Results indicated that the integration of fuzzy set theory with AHP produced significantly improved accuracies and a high level of reliability in the resulting landslide susceptibility map. Approximately 53% of known landslides within our study area fell within zones classified as having “very high susceptibility”, with the further 31% falling into zones classified as having “high susceptibility”.

Subfields: Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/33c7498eb993f616e58a42cc87ae67f0d0fa7363

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Citations in the corpus

A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, methodology): “It has been used as a standard method for validating landslidemodels inmany studies (Feizizadeh et al., 2014; Tien Bui et al., 2016).”
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection (2019, background): “Although some knowledge-based methods are independent of the existence of landslide inventory data sets for the generation of hazard and risk maps, the resulting maps require accuracy assessment and sensitivity analysis steps and, therefore, need accurate inventory data sets [18].”