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

Abstract

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.

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

“…In order to illustrate the trend of the slope, the aspect is classified into eight classes corresponding to eight geographic directions and an additional class for the flat ground. The relation between aspect and landslide occurrence has been widely (Bai et al 2010;Chen et al 2013;Chalkias et al 2014;Jebur et al 2014;Mashari et al 2012;Pourghasemi et al 2012;Akgun et al 2008;Constantin et al 2011;Sarkar and Kanungo 2004;Kayastha et al 2013). However, no clear agreement exists in the context of the aspect as a LCF.…”

“…2) as one of the major LCFs. There exists a close connection between elevation and landslide occurrence (Bai et al 2010;Chen et al 2013;Goetz et al 2015;Chalkias et al 2014;Jebur et al 2014;Mashari et al 2012;Pourghasemi et al 2012). All DEM-delivered LCFs are calculated based on the elevation which means that they can be represented by the elevation.…”

“…3) is the crucial landslide-conditioning factor because sliding of loose material is directly related to the slope (Bai et al 2010;Chen et al 2013;Goetz et al 2015;Chalkias et al 2014;Jebur et al 2014;Mashari et al 2012;Pourghasemi et al 2012;Akgun et al 2008;Constantin et al 2011;Thiery et al 2007;Sarkar and Kanungo 2004;Feizizadeh et al 2014Kayastha et al 2013Ozturk et al 2016). …”

“…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). Among multi-variate methods, logistic regression is widely applied (Bai et al 2010;Mashari et al 2012). The comparison of bivariate (statistical index) and multi-variate (logistic regression) methods was performed by Tien Bui et al (2011).…”

“…It shows the spatial distribution of landslide-prone areas, usually as landslide occurrence probabilities distributed across grid cells (Goetz et al 2015). Different methods of LSM have been broadly examined and analysed in the past decades Goetz et al 2015;Bai et al 2010;Mashari et al 2012;Tien Bui et al 2011, Feizizadeh et al 2014Dimri et al 2007;Kanungo et al 2008). Moreover, numerous comparisons of LSM methods have been evaluated and still no single best method has been selected (Goetz et al 2015).…”