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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

Final landslide susceptibility map.
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f0045: Final landslide susceptibility map.

Mentions: The final landslide susceptibility map was produced using the results from the two stages described in Section 4 above, in the following way:(27)LSMAHP=(slopedegree×WAHP)+(aspect×WAHP)+(distancetostream+WAHP)+(drainagedensity+WAHP)+(distancetofaults+WAHP)+(precipitation+WAHP)+(distancetoroads+WAHP)+(Lithology+WAHP)+(Landus/cover+WAHP)where WAHP is the respective weight for the each of the LSM criteria. The resulting landslide susceptibility map was then divided into five susceptibility categories (very low, low, moderate, high, and very high) using the natural breaks method to determine the class intervals (Feizizadeh and Blaschke., 2013a) (see Fig. 9).


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

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

Final landslide susceptibility map.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0045: Final landslide susceptibility map.
Mentions: The final landslide susceptibility map was produced using the results from the two stages described in Section 4 above, in the following way:(27)LSMAHP=(slopedegree×WAHP)+(aspect×WAHP)+(distancetostream+WAHP)+(drainagedensity+WAHP)+(distancetofaults+WAHP)+(precipitation+WAHP)+(distancetoroads+WAHP)+(Lithology+WAHP)+(Landus/cover+WAHP)where WAHP is the respective weight for the each of the LSM criteria. The resulting landslide susceptibility map was then divided into five susceptibility categories (very low, low, moderate, high, and very high) using the natural breaks method to determine the class intervals (Feizizadeh and Blaschke., 2013a) (see Fig. 9).

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