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

Feizizadeh B, Shadman Roodposhti M, Jankowski P, Blaschke T - Comput Geosci (2014)

Bottom Line: 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".

View Article: PubMed Central - PubMed

Affiliation: Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran.

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".

No MeSH data available.


Related in: MedlinePlus

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.
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f0010: 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.

Mentions: In order to generate a landslide susceptibility map, criteria need to be identified that are relevant to the particular situation under consideration. The set of criteria selected should adequately represent the problem domain and should contribute towards the ultimate objective (Prakash, 2003; Feizizadeh and Blaschke, 2013b). For our research we selected four main criteria (topography, hydrology, climate, and human factors) and nine sub-criteria (slope, aspect, distance to streams, distance to roads, drainage density, distance to faults, lithology, precipitation, and land use/land cover). Fig. 2 shows the spatial distributions of the selected sub-criteria.


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

Feizizadeh B, Shadman Roodposhti M, Jankowski P, Blaschke T - Comput Geosci (2014)

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.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4376179&req=5

f0010: 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.
Mentions: In order to generate a landslide susceptibility map, criteria need to be identified that are relevant to the particular situation under consideration. The set of criteria selected should adequately represent the problem domain and should contribute towards the ultimate objective (Prakash, 2003; Feizizadeh and Blaschke, 2013b). For our research we selected four main criteria (topography, hydrology, climate, and human factors) and nine sub-criteria (slope, aspect, distance to streams, distance to roads, drainage density, distance to faults, lithology, precipitation, and land use/land cover). Fig. 2 shows the spatial distributions of the selected sub-criteria.

Bottom Line: 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".

View Article: PubMed Central - PubMed

Affiliation: Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran.

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".

No MeSH data available.


Related in: MedlinePlus