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

Abstract

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.

Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support

“…Aleotti and Chowdhury, 1999;Guzzetti et al, 1999;Wang et al, 2005). The present study uses fuzzy logic as the method is computationally simple, can consider highly uncertain data inputs, has been shown to match or outperform other approaches (Pradhan, 2011;Bui et al, 2012;Pourghasemi et al, 2012), and, importantly for this study, is fast to apply. While other approaches, such as multivariate statistical analysis, may provide more accurate landslide forecasts, we argue that the marginal gain in forecast accuracy for such approaches is outweighed by the time required to undertake them.…”

“…This is especially true for lower income countries such as Nepal, where the necessary data either may not exist or may not be readily accessible. Further, we have shown that accurate landslide models can be created without the need for these factors, so long as other important factors, in particular slope angle, are considered (also see Pawluszek and Borkowski, 2017, for a comparison of the role of topographic factors and non-topographic factors). Of course, if these datasets do exist, then they can easily be incorporated into the factor analysis.…”

Reply to Reviewer 1 Comments

“…Aleotti and Chowdhury, 1999;Guzzetti et al, 1999;Wang et al, 2005). The present study uses fuzzy logic as the method is computationally simple, can consider highly uncertain data inputs, has been shown to match or outperform other approaches (Pradhan, 2011;Bui et al, 2012;Pourghasemi et al, 2012), and, importantly for this study, is fast to apply. While other approaches, such as multivariate statistical analysis, may provide more accurate landslide forecasts, we argue that the marginal gain in forecast accu-racy for such approaches is outweighed by the time required to undertake them.…”

Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

“…Qualitative models are mostly based on expert opinion, whereas quantitative models are data-driven, which makes them more reliable. The quantitative approaches include several kinds of techniques such, including statistical, deterministic, and other approaches [51][52][53]. In the case of statistical approaches, it is assumed that the parameters affecting landslide events in the past will be the same in future [54], and these analyses can be categorized into bivariate and multivariate [49].…”

“…In bivariate analysis, the factors affecting landslides are compared with landslide inventory data by providing weights based on landslide causative factors. The most frequently used methods in bivariate models are overlay, index-based, and weight-of-evidence analyses [8,51,55]. Bui et al [6] performed a comparison between a bivariate approach (statistical index) and a multivariate approach (logistic regression) for Vietnam, and found equal forecasting capability.…”