Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

Duman, Tamer Y.; Çan, Tolga; Gökçeoğlu, Candan; Nefeslioğlu, Hakan A.; Sönmez, H.

doi:10.1007/s00254-006-0322-1

Cited by 180 publications

(89 citation statements)

References 32 publications

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“…As mentioned by Duman et al (2006) and Yalcin (2008) observed landslides confirm that most of events are occurred near or close to the roads. The reason for this may relate to constructing roads with deep trenches.…”

Section: Discussionsupporting

confidence: 55%

Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran

Mashari¹,

Solaimani

Omidvar

2012

ENRR

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Landslide is a natural hazard that causes many damages to the environment. Depending on the landform, several factors can cause the Landslide. This research addresses the methodology for landslide susceptibility mapping using multiple regression analysis and GIS tools. Based on the initial hypothesis, ten factors were recognized as effectual elements on landslide, which is geology, slope, aspect, distance from roads, faults and drainage network, soil capability, land use and rainfall. Crossing investigated parameters with the observed landslides indicated that three factor including distance from channel network, distance from fault and rainfall have no major effect on observed landslide in Tajan area. In order to quantifying the parameters in the form of weighting factors, the coverage of landslides in different observation was determined. Then Stepwise method was used for statistical analysis. It was found that slope, aspect, distance from the roads and soil capability are as most effective factors in landslide respectively.

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Section: Discussionsupporting

confidence: 55%

Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran

Mashari¹,

Solaimani

Omidvar

2012

ENRR

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“…All the lithological units were categorized in view of their capability to trigger landslide Table 1. Each lithological unit has its own susceptibility towards landslides, so it needs to categorize lithological units accordingly [14]. Ranking of the rock types was based on their potential to cause landslide [1].…”

Section: Data Acquisition and Preparation Of Causative Factorsmentioning

confidence: 99%

GIS Based Landslide Susceptibility Mapping with Application of Analytical Hierarchy Process in District Ghizer, Gilgit Baltistan Pakistan

Rahim¹,

Ali²,

Aslam³

2018

GEP

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District Ghizer is a rugged mountainous territory which experiences several landslides each year. There are 16 major landslide areas and 53 villages that are at high risk to hazards. Keeping in view the severity of natural hazards, the present study was designed to generate landslide susceptibility map based on twelve causative factors viz., slope, aspect, elevation, drainage network, Stream Power Index (SPI), Topographic Wetness Index (TWI), lithological units, fault lines, rainfall, road network, land cover and soil texture. Soil texture was determined by particle size analysis and data for other factors were acquired from freely available sources. Analytical Hierarchy Process (AHP) was employed to identify major landslide causative factors in the district Ghizer. Further, a temporal assessment from 1999 till 2015 was generated to assess the impact of land cover change on landslides. It indicated that the barren soil/ exposed rocks and glaciers have reduced while the vegetation and water classes have shown increment. The total area that lies in moderate to very high landslide susceptible zones was 74.38%, while slope is the main landslide causative factor in the district Ghizer. Validation of the susceptibility map showed 88.1% of the landslides in the study area had occurred in the moderate to very high susceptible zones.

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“…The 69% correctly predicted landslide initiation areas show a good correspondence with another qualitative study, where Ruff and Czurda (2008) found 67% of actual landslide areas to be correctly predicted in high and very high susceptibility areas, using an expert evaluation approach. Compared to these qualitative studies and Duman et al (2006) found 63% and 80% of actual landslide areas to be correctly predicted in high and very high susceptibility areas using statistical approaches. Moreover, in this paper 87% of actual landslides were found in the overall predicted landslide susceptibility areas, which is consistent with results from other qualitative and quantitative studies.…”

Section: Discussionmentioning

confidence: 99%

A simple qualitative approach for mapping regional landslide susceptibility in the Faroe Islands

Dahl

Mortensen

Veihe

et al. 2010

Nat. Hazards Earth Syst. Sci.

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Abstract. The Faroe Islands in the North Atlantic Ocean are highly susceptible to landslides. Following recent landslide incidents, Jarðfeingi (Faroese Earth and Energy Directorate) has pointed out, that the risk of human lives or of property being lost or affected by landslides may be increasing. This paper aims at presenting and testing a simple qualitative approach for mapping regional landslide susceptibility in the Faroe Islands, using few key parameters. The susceptibility model holds information about both landslide initiation areas and runout zones. Landslide initiation areas are determined from slope angle thresholds (25 • -40 • ) and soil cover data, while runout zones are delineated using the angle of reach approach taking into account the presence/absence of geological benches in the runout path, which has not been considered in earlier studies. Data input is obtained from a landslide database containing 67 debris flows throughout the Faroe Islands. Angle of reach values differ significantly with the presence/absence of geological benches in the runout path. Two values of angle of reach, 21.5 • and 27.6 • , are used for calculating runout zones. The landslide susceptibility model is tested in a study area at the town of Klaksvík in the northern part of the Faroe Islands. A map validation comparing predicted susceptibility zones with a validation-dataset of 87 actual landslides in the study area reveal that 69% and 92%, respectively, of actual landslide initiation areas and runout zones are correctly predicted. Moreover 87% of the actual landslides are included in the overall predicted landslide susceptibility areas.Correspondence to: M.-P. J. Dahl (mpjd@ruc.dk)

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Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

Cited by 180 publications

References 32 publications

Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran

Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran

GIS Based Landslide Susceptibility Mapping with Application of Analytical Hierarchy Process in District Ghizer, Gilgit Baltistan Pakistan

A simple qualitative approach for mapping regional landslide susceptibility in the Faroe Islands

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