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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

A fuzzy triangular number (Kahraman et al., 2003).
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f0015: A fuzzy triangular number (Kahraman et al., 2003).

Mentions: A fuzzy set can be described as follows: if Z denotes a space of objects, then the fuzzy set (A) in (Z) is a set of ordered pairs:(1)A{z,MF(z)},z∈Zwhere the membership function is the set A’s degree of membership to Z. Fig. 3 shows the triangular fuzzy number (TFN) contains the basis for the membership function the TFNs are denoted simply by m1, m2, and m3. The parameters m1, m2 and m3 respectively denote the smallest possible value, the most promising value, and the largest possible value that describes a fuzzy object (Kahraman et al., 2003). Using this approach each TFNs has a linear representation on its left and right sides and the membership function can be defined as:(2)μ(x/M˜){0,x<m1(x−m1)/(m2−m1),m1≤x≤m2(m3−x)/(m3−m2),m2≤x≤m30,x<m3


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

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

A fuzzy triangular number (Kahraman et al., 2003).
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0015: A fuzzy triangular number (Kahraman et al., 2003).
Mentions: A fuzzy set can be described as follows: if Z denotes a space of objects, then the fuzzy set (A) in (Z) is a set of ordered pairs:(1)A{z,MF(z)},z∈Zwhere the membership function is the set A’s degree of membership to Z. Fig. 3 shows the triangular fuzzy number (TFN) contains the basis for the membership function the TFNs are denoted simply by m1, m2, and m3. The parameters m1, m2 and m3 respectively denote the smallest possible value, the most promising value, and the largest possible value that describes a fuzzy object (Kahraman et al., 2003). Using this approach each TFNs has a linear representation on its left and right sides and the membership function can be defined as:(2)μ(x/M˜){0,x<m1(x−m1)/(m2−m1),m1≤x≤m2(m3−x)/(m3−m2),m2≤x≤m30,x<m3

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