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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 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.
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f0040: 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.

Mentions: There is no optimal method for choosing the most appropriate FMFs and their respective parameters and they are generally selected according to the preferences of the decision makers or analyst experience. In this process, sigmoidal membership functions (i.e., monotonically decreasing and monotonically increasing), user-defined linear membership functions, two crisp membership functions, are specified for selected landslide criteria (see Fig. 7). The sigmoidal membership function is probably the most commonly used function in fuzzy set theory (Zadeh, 1965; Eastman, 2004; Liu et al., 2004). although user-defined linear FMFs or crisp membership functions are also sometimes used. Here, regarding the inherent characteristics of lithology and land use/land cover criteria two different crisp membership function (i.e. two look up table) were implemented for further standardization of those mentioned criteria (see Fig. 7). To this end, less susceptibility value is assigned to the less susceptible formation or land used/ land cover class, and vice versa. All membership functions obtained from LSM criteria outputs are applied to each parameter, which are then classified into groups on the basis of their landslide susceptibilities (see Fig. 8).


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

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

f0040: 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.
Mentions: There is no optimal method for choosing the most appropriate FMFs and their respective parameters and they are generally selected according to the preferences of the decision makers or analyst experience. In this process, sigmoidal membership functions (i.e., monotonically decreasing and monotonically increasing), user-defined linear membership functions, two crisp membership functions, are specified for selected landslide criteria (see Fig. 7). The sigmoidal membership function is probably the most commonly used function in fuzzy set theory (Zadeh, 1965; Eastman, 2004; Liu et al., 2004). although user-defined linear FMFs or crisp membership functions are also sometimes used. Here, regarding the inherent characteristics of lithology and land use/land cover criteria two different crisp membership function (i.e. two look up table) were implemented for further standardization of those mentioned criteria (see Fig. 7). To this end, less susceptibility value is assigned to the less susceptible formation or land used/ land cover class, and vice versa. All membership functions obtained from LSM criteria outputs are applied to each parameter, which are then classified into groups on the basis of their landslide susceptibilities (see Fig. 8).

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