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. 2014 Dec:73:208-221.
doi: 10.1016/j.cageo.2014.08.001.

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

Bakhtiar Feizizadeh  1 , Majid Shadman Roodposhti  2 , Piotr Jankowski  3 , Thomas Blaschke  4
Affiliations

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

Bakhtiar Feizizadeh et al. Comput Geosci. 2014 Dec.

Abstract

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".

Keywords: Fuzzy-AHP; GIS based MCDA; Izeh River basin; Landslide susceptibility maps; Membership functions.

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Figures

Fig. 1
Fig. 1
Location of the study area.
Fig. 2
Fig. 2
Spatial distribution of the selected criteria: (a) slope, (b) aspect, (c) distance to streams, (d) drainage density, (e) distance to faults, (f) precipitation, (g) distance to roads, (h) lithology, and (i) land use/land cover.
Fig. 3
Fig. 3
A fuzzy triangular number (Kahraman et al., 2003).
Fig. 4
Fig. 4
The degree of possibility V(S˜i≥S˜j) (Vahidnia et al., 2009).
Fig. 5
Fig. 5
TFNs corresponding to linguistic variables representing levels of preference (Vahidnia et al., 2009).
Fig. 6
Fig. 6
Schematic representation of proposed LSM.
Fig. 7
Fig. 7
FAHP-based membership functions including: (Type I) user defined FMFs for (a) slope and (b) aspect, (Type II) Sigmoidal FMFs for (c) distance to streams, (d) drainage density, (e) distance to faults, (f) precipitation, (g) distance to roads, and (Type III) Crisp MFs for (h) lithology and (i) land use/cover.
Fig. 8
Fig. 8
Spatial distribution of landslide susceptibility for each criterion, based on fuzzy membership functions (i.e. fuzzy or crisp) of each parameter: (a) slope, (b) aspect, (c) distance to streams, (d) drainage density, (e) distance to faults, (f) precipitation, (g) distance to roads, (h) lithology, and (i) land use/cover.
Fig. 9
Fig. 9
Final landslide susceptibility map.
Fig. 10
Fig. 10
ROC curve for the obtained landslide susceptibility map.
Fig. 11
Fig. 11
Validation of landslide susceptibility map using known landslides in the study area.
Fig. 12
Fig. 12
Illustration of data loss due to crisp standardization process in geographic information systems.
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