Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models

Akgün, Aykut; Dağ, Serhat; Bulut, Fikri

doi:10.1007/s00254-007-0882-8

Cited by 313 publications

(138 citation statements)

References 60 publications

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“…Nevertheless, it should be stressed that, as pointed out by Akgun et al (2008), both urbanized and cultivated areas result from heavier modifications of the original landscape, and the instability phenomena could be triggered by such modifications. In addition, the two classes are inclined to be geographically linked because of their presence where the morphological pattern allows anthropogenic alterations to be made.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, in addition to making a landslides inventory in the investigated area, the following ten causal factors were selected: altitude, slope angle, slope exposure, planform curvature, profile curvature, lithology, land cover, drainage basin, distance from roads and distance from rivers. All these data, that will be examined in the following sections, were initially available in vector or raster formats and their processing and manipulation was entirely managed in the GIS environment (Akgun et al, 2008;Ayalew and Yamagishi, 2005;Lee and Min, 2001;Lee and Pradhan, 2007;Nandi and Shakoor, 2009;Santacana et al, 2003;Vijith and Madhu, 2008;Yesilnacara and Topalb, 2005). The selection of variables with a major role in landslides susceptibility analysis can be a very difficult task.…”

Section: Causal Factors Used In the Lr Analysismentioning

confidence: 99%

“…In Ayalew et al (2005a) an interesting summary of the most commonly used methods for landslide susceptibility analysis can be found, together with a complete reference list. Relevant studies have also been proposed by Lee and Sambath (2006), who compared the use of frequency ratio and LR models, , who added studies related to the use of artificial neural networks and Akgun et al (2008), where the likelihood of frequency ratio and a weighted linear combination model are compared. Recently, Ayalew et al (2005b) introduced the use of a couple of methods for landslide susceptibility mapping: the first using bivariate statistical analysis to classify quantitative variables and the second, based on the Analytic Hierarchy Process (AHP), to assign weights to the attributes.…”

Section: Introductionmentioning

confidence: 99%

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GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

Mancini

Ceppi

Ritrovato

2010

Nat. Hazards Earth Syst. Sci.

122

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Abstract. This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

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Section: Resultsmentioning

confidence: 99%

Section: Causal Factors Used In the Lr Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

Mancini

Ceppi

Ritrovato

2010

Nat. Hazards Earth Syst. Sci.

122

View full text Add to dashboard Cite

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“…These techniques apply primarylevel weights, which are commonly based on certain rules. The widely used rule is landslide density, which is calculated as relation between the area affected by landslide pixels on a class of a specific factor and the total area of that class; expressed as a percentage (Ayalew et al 2004;Akgun et al 2008). The most frequently used bivariate methods are frequency ratio calculation, also called the landslide susceptibility index, probabilistic likelihood ratio (PLR), weight of evidence and statistical index (Kavzoglu et al 2015a, b;Thiery et al 2007;Mezughi et al 2011;Yalcin et al 2011;Abay and Barbieri 2012;Constantin et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland

Pawłuszek

Borkowski

2016

Nat Hazards

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Choosing appropriate landslide-controlling factors (LCFs) in landslide susceptibility mapping (LSM) is a challenging task and depends on the nature of terrain and expert knowledge and experience. Nowadays, it is very common to use digital elevation model (DEM) and DEM-derivatives, as a representation of the topographic conditions. The objective of this study is to explore topography in depth and simultaneously reduce redundant information within DEM-derivatives using principal component analysis. Moreover, this study investigates the impact of DEM-derived factors on LSM. Therefore, three various strategies were tested. The first strategy included a set of LCFs created from the four initial principal components, which were provided from DEM-derived factors. The second strategy included a set of parameters which contained additional lithological and environmental factors. The third strategy utilises the analytical hierarchy process (AHP) to assign weights to each LCF. The LSM was performed based on landslide susceptibility index. Obtained results show that 60% of existing landslides fell into high and very high susceptibility zones using first and second strategies. It proves that topographic factors play a significant role in LSM. Adding additional lithological and environmental factors to the set of LCFs did not improve the results significantly, unless the AHP was used in the third strategy. It improved results significantly; up to 70%. Results from second and third strategies highlight utility of AHP in LSM. Presented studies were performed on the area very prone to landslide occurrence in the region of Ro_ znów Lake, Poland.

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“…Recently, there have been studies on landslidehazard evaluation using GIS, and many of these studies have applied probabilistic models (Akgun et al 2007;Dahal et al 2007;Clerici et al 2006;Cevik and Topal 2003;Rowbotham and Dudycha 1998;Jibson et al 2000;Luzi et al 2000;Parise and Jibson 2000;Baeza and Corominas 2001;Clerici et al 2002;Donati and Turrini 2002;Zhou et al 2002;Lee et al 2004b). 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.…”

Section: Introductionmentioning

confidence: 99%

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

2009

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Geospatial data base creation for landslidehazard mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslidehazard analysis was modeled on the basis of spatially related factors. This paper presents the use of fuzzy logic to landslide-hazard analysis in the Penang Island, Malaysia, using remote sensing data and a geographic information system (GIS). To achieve the goal, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined for landslide-hazard analysis. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and field surveys. The nine factors that influence landslide occurrence were extracted from the database and the frequency ratio coefficient for each factor was computed. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslidehazard mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ=0.8) showed the best accuracy (80%) while the case in which the fuzzy or operator was applied showed the worst accuracy (56%).

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Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models

Cited by 313 publications

References 60 publications

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland

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

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