Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran

Abstract

The northwestern Fars province of Iran is prone to landslides. In order to help the planners in the selection of suitable locations to implement development projects, a landslide susceptibility map has been produced for the Kakan catchment area using the gamma aggregation operation of the fuzzy approach. Lithology, slope angle, slope aspect, land cover, weathering depth, proximity to roads, topographical elevation, and soil depth were considered as landslide causal factors for the study area. Three values for gamma were examined and output maps were evaluated using the known landslides. Performing fuzzy gamma operation with 0.94 for gamma and classifying the area yielded an output susceptibility map with four zones: non-susceptible, moderate susceptibility, high susceptibility, and very high susceptibility. Gamma values <0.94 decreased the fuzzy membership functions to low susceptibility zones, and eliminated the high and very high classes. Gamma values >0.94 moved the output fuzzy membership function values to high and very high landslide susceptibilities and eliminated the moderate susceptible zones. Comparing the known landslides of the area with the output susceptibility map showed that 93.9% of the identified landslides were located in the high susceptible zone and 6.1% in very high susceptible zone.

Fire risk evaluation using multicriteria analysis—a case study

“…For example, MCDM methods have been developed to solve conflicting preferences among criteria (Keeney and Raiffa 1976). Rational decisionmaking requires combining both objective and subjective criterion (Pomerol and Barba-Romero 2000), most notably in a collaborative participatory framework for which MCDM methods can provide useful framework (Saaty 1994;Malczewski 2002;Tangestani 2004;Sadiq and Husain 2005). The study area is dominated by forests, where most of the stakeholders are local indigenous people, and their dependence on forest resources is immense.…”

“…Integration of fuzzy logic with GIS in a decision-making framework has been used for different purposes, including land suitability based upon soil profiles (Burrough et al 1992), soil classification (Lark and Bolam 1997), landfill site screening (Charnpratheep et al 1997), soil erosion (Mitra et al 1998), crop land suitability analysis (Ahmed et al 2000), ranking burned forests to evaluate the risk of desertification (Sasikala and Petrou 2001), seeking optimum locations for real estate (Zeng and Zhou 2001), assessing vulnerability to natural hazards (Rashed and Weeks 2003;Tangestani 2004;Dixon 2005), estimating risk (Sadiq and Husain 2005), incorporating farmer's knowledge for land suitability classification (Sicat et al 2005), fuel type mapping (Nadeau and Englefield 2006), assessing spatial extent of dry land salinity (Malins and Metternicht 2006), etc. Therefore, many studies have been performed using fuzzy logic integrated with GIS in a MCDM framework demonstrating that the methods are robust and valid.…”

Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping

“…One of the statistical models available, the logistic regression models, has also been applied to landslide-hazard mapping (Tunusluoglu et al 2007;Lamelas et al 2008;Wang and Sassa 2005;Süzen and Doyuran 2004;Dai and Lee 2002;Ohlmacher and Davis 2003;Mansor et al 2007;Pradhan et al 2006, as has the geotechnical model and the safety factor model (Gokceoglu et al 2000;Romeo 2000;Refice and Capolongo 2002;Carro et al 2003;Shou and Wang 2003;Zhou et al 2003, Lee 2007. As a new approach to landslide-hazard evaluation using GIS, data mining, safety factor, fuzzy logic and artificial neural network models have been applied (Gomez and Kavzoglu, 2005;Xie et al 2004;Ercanoglu and Gokceoglu 2002;Pistocchi et al 2002;Lee et al 2003aLee et al , b, 2004aTangestani 2004;Lee and Pradhan 2006;Lee 2007, 2008 ;Catani et al 2005).…”

Predicting favourable areas for landsliding through GIS modelling in Aparados da Serra (Brazil)

“…GIS development enhanced landslide susceptibility analysis in different ways (e.g. [5,6,7]). It seems that an inventory of historical data is a key factor in landslide susceptibility map construction.…”