Landslide Hazard and Risk Mapping Using the Weighted Linear Combination Model Applied to the Tevankarai Stream Watershed, Kodaikkanal, India

Spatial Prediction of Erosion Risk of a Small Mountainous Watershed Using RUSLE: A Case-Study of the Palar Sub-Watershed in Kodaikanal, South India

“…This sequence of factors makes agricultural lands susceptible to degradation, promotes soil loss and deters soil formation, and highlights the need to identify unsuitable land management practices that damage soil quality and impede sustainable agricultural uses of soil [13,38,48,49] Educating farmers on good agricultural practices such as the use of organic manures and pesticides, providing know-how on healthy irrigation methods like drip irrigation, and subsidies for good farming practices can help combat soil loss in agricultural lands and plantations. Terraces can be protected by hedgerow grasses like Napier grass (Pennisetum purpureum, family: Poaceae), Leucaenea leucocephala and asparagus [27]. Minimal severe erosion was noticed in built-up areas where slopes can be riveted with pebbles or stones to control soil wash off; asparagus grass can also be grown on these slopes.…”

“…Bedrock geology is fairly monotonous, comprising charnockite in varying degrees of weathering. A small segment of fissile hornblende gneiss is observed in the south [27]. The exposed charnockite massif shows a close association with pink granite, through sharp and gradational contrast.…”

A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the Flow-R model for landslide susceptibility assessment

“…In the past few years, a literature review has shown that the weight of evidence model (WoE) has been applied efficiently and widely in landslide susceptibility mapping (Dahal et al 2008a(Dahal et al , 2008bOh and Lee 2010;Regmi et al 2010;Xu et al 2012; Pourghasemi Pradhan, Gokceoglu, Mohammadi, et al 2013;Sujatha et al 2014;Guo et al 2015;Wang et al 2016). The analytical hierarchy process approach (AHP) has also been applied widely in landslide prediction (Yalcin 2008;Yalcin et al 2011;Long and De Smedt 2012;Pourghasemi et al 2012;Kayastha et al 2013;Pourghasemi, Moradi, and Fatemi Aghda 2013;Sujatha and Rajamanickam 2015;Patriche et al 2016;Zhang et al 2016). One of the most popular models in multivariate statistical analysis, logistic regression (LR) has also been developed and applied for landslide susceptibility mapping (Ayalew and Yamagishi 2005;Lee and Sambath 2006;Lee and Pradhan 2007;Sujatha et al 2011;Choi et al 2012;Althuwaynee et al 2014;Kavzoglu et al 2014;Hong et al 2015;Erener et al 2016;Hong et al 2016;Martinov ıc et al 2016;Zezere et al 2017).…”

Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models

“…Globally, several probability models, combined with GIS, have been used for landslide susceptibility assessment, such as weights of evidence (Oh and Lee 2011;Ozdemir and Altural 2013;Sujatha et al 2014;, evidential belief function (EBF) (Tien Bui et al 2013;Pradhan et al 2014) and frequency ratio (Lee et al 2004;Lee and Pradhan 2006;Dahal et al 2008;Sujatha et al 2013). Statistical models have also been used for landslide susceptibility assessment, including statistical index (Constantin et al 2011;Aghdam et al 2016;Chen, Chai, Sun, et al 2016), analytical hierarchy process (Demir et al 2013;Shahabi et al 2014;Chen, Li, et al 2015;Sujatha and Rajamanickam 2015) and logistic regression (LR) models (Lee 2007;Sujatha et al 2011;Demir et al 2013;Devkota et al 2013;Ozdemir and Altural 2013). Because the prediction capabilities of these proposed models are critical, machine learning models have also been investigated, such as fuzzy logic (Pradhan 2010;Pourghasemi et al 2012;Guettouche 2013), fuzzy rule-based classifier, neuro-fuzzy (Pradhan 2013;Dehnavi et al 2015), multivariate adaptive regression splines (Vorpahl et al 2012;Felic ısimo et al 2013;Conoscenti et al 2015), neural network (Lee 2007;Yilmaz 2009;Park et al 2013;Tien Bui et al 2016) and support vector machine (SVM) models (Xu et al 2012;Chen, Chai, Zhao, et al 2016;Colkesen et al 2016).…”