Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China

Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization

“…The random forest is a technique that has been applied to landslide susceptibility only recently (Brenning 2005;Catani et al 2013); however, it can be considered well-established because its use has been consolidated through many applications in different case studies and because it has often shown better perform a n c e s w h e n c o m p a r e d w i t h o th e r s t a t e -o f -t h e -a r t methodologies (Trigila et al 2013;Xiao et al 2019). In addition, it is very flexible and straightforward to apply, because it can handle at the same time both numerical and categorical variables, it implicitly accounts for mutual dependency between variables, it reduces overfitting, and it does not require particular assumptions on the statistical distribution of the values of the data.…”

“…Concerning the independent variables, there is no consensus on the number of parameters to use in a susceptibility assessment: many studies have been published that make use of a high number of parameters (Lee and Pradhan 2007;Nefeslioglu et al 2011;Segoni et al 2015;Xiao et al 2019) and many that use only a few (Hong et al 2007;Akgun 2012;Manzo et al 2013). In this study, we decided to use a very basic and reduced set of parameters, because the objective is to explore the sensitivity to geology and to focus the discussion on the impact of this parameter.…”

“…In the recent literature, it is possible to identify two main methodologies to use geological information in LSM: (i) preexisting thematic maps about the nature of the bedrock are retrieved and used without further elaborations (Pourghasemi and Kerle 2016;Pradhan et al 2019;Xiao et al 2019); (ii) existing detailed digital geological maps undergo a reclassification aimed at reducing the number of classes and at defining a stronger correlation with landsliding. This approach is used especially when working over large or complex areas, where a too detailed geological information needs to be generalized (Bălteanu et al 2010;Van Den Eeckhaut et al 2012;Trigila et al 2013).…”

“…The drawback is that spatially distributed maps of geotechnical properties of surface deposits are usually unavailable, with the exception of some recent studies (e.g., Bicocchi et al 2019). Other studies use also geological units, which are mainly based on the age of deposition of the units (Van Den Eeckhaut et al 2012;Xiao et al 2019) even if in local-scale studies this could also partially or totally reflect lithological aspects (Youssef et al 2016;Cárdenas and Mera 2016;Pourgashemi and Kerle 2016). Besides, a number of alternate approaches can be found in the recent literature about landslide susceptibility mapping: as instance, Meinhardt et al (2015) use a broad classification into 5 rock types subdivided by the different genesis (e.g., loose soils and metamorphic, intrusive, extrusive, and sedimentary rocks); Myronidis et al (2016) make use of units classified based on the geological structure; Camarinha et al 2014, to study shallow landslide susceptibility in a test site in Brazil, used a rock-type classification based on a complex criterion encompassing geology, lithology, and pedology.…”

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

“…Some of them are based on statistical rules, which aim to determine the importance of each independent landslide factor through assigning weights. Novel intelligent models are also proposed for approximating the susceptibility of an area through learning the mathematical relationship between a landslide and its related factors [7,8].…”