An Assessment of Karst Collapse Hazards in Guilin, Guangxi Province, China

Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain)

“…Although the goodness of fit (fitting-rate) between the distribution of sinkholes used in the analysis and that of the susceptibility zones of the prediction models have been calculated by several authors (Edmonds, 2001;Yilmaz, 2007;Dai et al, 2008;Kaufmann, 2008), the evaluation of the predictive capability of the models using independent sinkhole populations has only been performed by Galve et al (2008a,b) and Lamelas et al (2008). Consequently, the reliability of most of the susceptibility models presented in the literature remains unknown.…”

“…These indirect methods can be divided into (1) heuristic and (2) statistical or probabilistic (Hansen, 1984;Carrara et al, 1995). Heuristic models base susceptibility assessments on the establishment, in a rather subjective way, of threshold values (Gao and Alexander, 2003) or a scoring system to a group of conditioning factors (Ogden, 1984;Thorp and Brook, 1984;Brook and Allison, 1986;van Rooy, 1989;Buttrick, 1992;Forth et al, 1999;Edmonds, 2001;Zisman, 2001;Kaufmann and Quinif, 2002;Zhou et al, 2003;Lei et al, 2005;Tolmachev and Leonenko, 2005;Dai et al, 2008;Koutepov et al, 2008). Probabilistic methodologies derive the susceptibility models from the analysis of statistical relationships between the known sinkholes and a group of conditioning factors.…”

Collapse dolines susceptibility mapping in Doukkala Abda (Western Morocco) by using GIS matrix method (GMM)

“…Furthermore, any susceptibility model or map must be validated by two methods (Guzzetti et al 2006); i.e., the validation of a collapse doline susceptibility model can be ascertained using the same collapse doline data used to obtain the susceptibility assessment (Edmonds 2001;Kaufmann and Quinif 2002;Yilmaz 2007;Dai et al 2008), or by using independent collapse dolines information (Galve et al 2008;Lamelas et al 2008) not available to build the model of collapse dolines susceptibility map. In the anterior works which were carried out in the study area (Mazéas 1967;Theilen-Willige et al 2014), the dolines susceptibility models were generated but not tested.…”

Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy)

“…Probabilistic and statistical methods have been extensively applied in landslide susceptibility analyses (e.g., Calligaris et al, 2013;Petschko et al, 2014;Piacentini et al, 2015;Steger et al, 2016), but they are not very common in the literature related to karst subsidence sinkholes (see Table 1). The evaporite karst of the Ebro Valley (Spain) is the region where these methods have often been tested (Simón et al, 1991;Soriano & Simón, 1995;Simón & Soriano, 2002;Lamelas et al, 2008;Galve et al, 2008Galve et al, , 2009bGalve et al, , 2011, but other authors also used these techniques to produce sinkhole susceptibility maps in other study areas (Hyland, 2005;Yilmaz, 2007;Dai et al, 2008;Doctor et al, 2008;Oh & Lee, 2010;Nachbaur & Rohmer, 2011;Doctor & Doctor, 2012;Papadopoulou-Vrynioti et al, 2013;Pradhan et al, 2014;Cahalan, 2015;D'Angella et al, 2015;Ciotoli et al, 2016; Neural Networks and Weights of Evidence. The present paper describes the application of a method based on the Favorability Functions approach (Chung & Fabbri, 1993), and the Likelihood Ratio (Chung, 2006).…”