A Fuzzy-based Methodology for Landslide Susceptibility Mapping

Barrile, Vincenzo; Cirianni, Francis; Leonardi, Giovanni; Palamara, Rocco

doi:10.1016/j.sbspro.2016.05.309

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…The selection of conditioning factors needs to take the characteristics of the study area and data availability into account (Khosravi et al 2016a). Based on the literature review, the study area condition and data availability (Althuwaynee et al 2014;Hong et al 2015;Sahin et al 2015;Barrile et al 2016), 17 conditioning factors have been identified which are practical and applicable for the study area including: geological factors (lithology, distance from faults, LULC and Differential Vegetation Index (DVI)), geomorphological factors (elevation, slope aspect, slope angle, tangential curvature, profile curvature and plane curvature), hydrological factors (distance from drainage, rainfall, Stream Power Index (SPI), Sediment Transport Index (STI) and temperature), and anthropogenic factors (distance from road, density of settlement).…”

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

et al. 2019

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This study presented the prediction capability of three methods including the frequency ratio (FR), fuzzy gamma (FG) and landslide index method (LIM) to produce landslide-prone areas in the Sari-Kiasar watershed, Mazandaran Province of Iran. In the first step, 105 landslide locations were selected and were randomly divided into two groups of 75% (78 locations) and 25% (27 locations) as training and testing datasets. Then the 17 landslide conditioning factors including land use/land cover, Differential Vegetation Index (DVI), lithology and distance from faults, elevation, slope aspect, slope angle, tangential curvature, profile curvature and plane curvature, distance from drainage, rainfall, Stream Power Index, Sediment Transport Index and temperature, and distance from road, density of settlement were considered for the proposed modelling approach. Finally, by applying the training dataset, three landslide susceptibility maps were constructed by using the FR, FG and LIM methods. The prediction capability of the performed model was evaluated by the area under the receiver operating curve or AUC for both training (success rate) and testing (prediction rate) datasets. The results showed that the AUC for success rate of FR, FG and LIM models was 82.04%, 81.08% and 73.61% and for prediction rate was 82.72%, 79.09% and 65.45%, respectively. The results showed that the FR model has a higher prediction accuracy than the FG and LIM methods. This study revealed that the most important factors in landslide occurrence are rainfall, slope and vegetation. The result of the present study can be possibly useful for land use planning and watershed management.

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Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

et al. 2019

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“…[20][21][22][23] Barrile et al. 24 used GIS-based fuzzy logic in order for road network planning. 24 Feizizadeh et al 23 applied fuzzy and AHP approaches for landslide susceptibility mapping and the output was considered as satisfactory due to 53% overlap with observed landslide layer.…”

Section: Introductionmentioning

confidence: 99%

“…24 used GIS-based fuzzy logic in order for road network planning. 24 Feizizadeh et al 23 applied fuzzy and AHP approaches for landslide susceptibility mapping and the output was considered as satisfactory due to 53% overlap with observed landslide layer. 25 Bui et al used the GIS-based fuzzy neural network for landslide susceptibility mapping and through testing multiple membership functions figured out that Gaussian membership function was the most suitable function Kayastha et al 26 applied AHP for landslide susceptibility mapping and according to the reliable outcome, they considered AHP as a reasonable approach Althuwaynee et al 27 compared AHP Logistic regression, and Bayesian methods in landslide susceptibility mapping, and found AHP as the most reliable method for criteria rating.…”

Section: Introductionmentioning

confidence: 99%

Landslide modelling and susceptibility mapping using AHP and fuzzy approaches

Noorollahi

Sadeghi

Yousefi

et al. 2018

IJH

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Landslide is one of the most destructive natural hazards that can impose economical and life loss. Fortunately, through numerical and spatial analysis we can lessen the damages caused by landslides. In this study which goes through landslide susceptibility mapping, a combination of AHP and Fuzzy approaches is applied. AHP is used in order to prioritize the effective criteria and Fuzzy method is used in two steps, factor map fuzzification, and factor maps are calculated and overlaid after fuzzification. In order to achieve the best output susceptibility map, 6 scenarios has been proposed in fuzzy overlay step for a study area in Iran as a case study and the best scenario has been chosen according to the comparison of every output with observed landslides layer. The susceptibility map that has the greatest overlap area was the result of using fuzzy Or in overlap step which is considered as a suitable way in hazard risk zonnation. Analysis of the final landslide susceptibility map shows that scenario 6 which is proven to be the most suitable outcome, has the least AUC= 0.3 and matches with observed landslides much better than other scenarios, and through physiographical analysis, we found that most landslides have occurred and are probable in are used as good ranges, are in middle slope angle, and have northern slope aspects.

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“…Thiery et al, (2006) used fuzzy logic to evaluate landslide-prone areas in the northern foothills of the Alps in France and introduced the use of fuzzy logic due to the high accuracy and measurement of outputs with proper definition of fuzzy logic operators and combination of sum and  operators for generation of landslide map as the best combination. (Barrile et al, 2016;Akgun et al, 2012;Pourghasemi et al, 2012) some studies used fuzzy membership function to prepare landslide hazard zonation map and introduced fuzzy Logic due to the coordination of data and also for additional flexibility of spatial analysis process as very efficient and useful method in preparation of landslide hazard mapping. Due to the fact that a large part of Iran's area is mountainous, there are many areas susceptible to mass movement occurrences and many researchers are trying to provide different methods for identification and zoning of these natural hazards (Pourghasemi et al, 2012;Ghanavati et al, 2015;Pourghasemi et al, 2016;Vakhshoori and Zare, 2016;Aghdam et al, 2017;Gheshlaghi and Feizizadeh, 2017).…”

Section: Introductionmentioning

confidence: 99%

COMPARISON of FUZZY-BASED MODELS in LANDSLIDE HAZARD MAPPING

Mijani

Samani

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR) and Quality Sum (QS). The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P) and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzygamma method with a minor difference is in the second order.

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A Fuzzy-based Methodology for Landslide Susceptibility Mapping

Cited by 12 publications

References 8 publications

Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

Landslide modelling and susceptibility mapping using AHP and fuzzy approaches

COMPARISON of FUZZY-BASED MODELS in LANDSLIDE HAZARD MAPPING

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