Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models

Mohammady, Majid; Pourghasemi, Hamid Reza; Pradhan, Biswajeet

doi:10.1016/j.jseaes.2012.10.005

Cited by 412 publications

(160 citation statements)

References 70 publications

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“…10) is used in the landslide susceptibility studies as the meaningful hydrological factor (Pourghasemi et al , 2013Akgun et al 2008;Mohammady et al 2012). However, other investigations (Gokceoglu et al 2005) treat SPI (Fig.…”

Section: Stream Power Index (Spi)mentioning

confidence: 99%

“…Many researchers claim that the presence of roads in mountainous areas increases the chance of landslide subsistence (Akgun et al 2008;Feizizadeh et al 2014;Pourghasemi et al 2012Pourghasemi et al , 2013Jebur et al 2014;Mashari et al 2012;Mohammady et al 2012). Moreover, road constructions may undercut the slopes and break the rock structures, consequently decreasing the slope strengths.…”

Section: Distance From Roadsmentioning

confidence: 99%

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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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Section: Stream Power Index (Spi)mentioning

confidence: 99%

Section: Distance From Roadsmentioning

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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“…Quantitative methods mainly refer to various statistical analyses (Carrara et al 1991;Tang et al 2011a;Yoshimatsu and Abe 2006;Kamp et al 2008), such as artificial neural network (ANN) (Lee and Evangelista 2006;Yilmaz 2010), support vector machine (SVM) (Yao et al 2008;Yilmaz 2010;Xu et al 2012), Bayesian Network (Song et al 2012) and Logistic Regression (Dong et al 2011;Nefeslioglu et al 2006;Xu et al 2013a, b). These methods have different characteristics and adaptability for various environmental conditions of different regions (Tangestani 2009;Mohammady et al 2012;Ozdemir and Altural 2013). At the same time, GIS and RS technologies have comprehensively been used in the landslide susceptibility study Lan et al 2002Lan et al , 2004Kouli et al 2010;Niu et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

et al. 2014

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The Lushan earthquake with magnitude M s 7.0 (M w 6.6, USGS) in Sichuan Province, China, triggered a large number of landslides, which seriously aggravated the earthquake's destructive consequences. This paper mainly focuses on the methodology of the urgent landslide susceptibility assessment right after the earthquake. The detailed landslide inventory (including 5,688 landslides) is prepared by means of urgent postearthquake landslide field survey, landslide remote sensing interpretation of multi-source remote sensing images including high-resolution unmanned aerial vehicle images and historical landslide archives. Ten remarkable causative factors for landslide occurrence have been selected to conduct the landslide susceptibility assessment, including earthquake intensity, landslide density and slope gradient. An integration assessment approach is developed to facilitate the effective urgent post-earthquake landslide susceptibility assessment using three methods: factor sensitivity analysis, analytical hierarchy process and factor-weighted overlay. Such integration can effectively reduce the subjectivity and uncertainty resulting from using single method. The validation evaluation using the area under curve suggests the landslide susceptibility assessment results have satisfactory accuracy, and the suggested methodology is effective for the urgent post-earthquake landslide susceptibility assessment. The study results reveal that earthquake intensity and slope gradient are the two most important causative factors for post-earthquake landslide occurrence in the Lushan earthquake-impacted area. The dominant slope gradient and slope aspect with relatively higher landslide frequency are 45°-50°and south-east direction, respectively. The intense earthquake impact increased the dominant slope gradient of 123Nat Hazards (2015) 75:2467-2487 DOI 10.1007/s11069-014-1441 landslide spatial distribution, and the thrust campaign of seismogenic fault with strike NE-SW made south-east direction as the dominant slope aspect of the landslide spatial distribution. The locations with very high and high landslide susceptibility are mainly distributed in the regions with higher earthquake intensity and adverse terrain conditions, such as Shuangshi town and Longmen town of Lushan county and Muping town of Baoxing county. The study results are expected to provide a beneficial reference for the landslide prevention and infrastructure reconstruction after the Lushan earthquake.

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“…The main land use types in this watershed are broadleaf forest, conifer forest, rangeland, agricultural land, water bodies and residential land. The Baghsalian watershed is subject to natural hazards such as floods and landslides, which makes land use mapping essential in terms of future planning (Mohammady et al 2012). …”

Section: Introductionmentioning

confidence: 99%

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

Mohammady

Moradi

Zeinivand

et al. 2014

Int. J. Environ. Sci. Technol.

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Land use classification is often the first step in land use studies and thus forms the basis for many earth science studies. In this paper, we focus on low-cost techniques for combining Landsat images with geographic information system approaches to create a land use map. In the Golestan region of Iran, we show that traditional supervised and unsupervised methods do not result in sufficiently accurate land use maps. Therefore, we evaluated a synthetic approach combining supervised and unsupervised methods with decision rules based on easily accessible ancillary data. For accuracy assessment, confusion matrices and kappa coefficients were calculated for the maps created with the supervised, unsupervised and synthetic approaches. Overall accuracy of the synthetic approach was 98.2 %, which is over the 85 % level that is considered satisfactory for planning and management purposes. This shows that integration of remote sensing data, ancillary data and decision rules provides better classification accuracy than traditional methods, without significant additional use of resources.

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Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models

Cited by 412 publications

References 70 publications

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

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

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

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