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Open AccessArticle

Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, 50-375 Wroclaw, Poland
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Author to whom correspondence should be addressed.
These authors contribute equally to this paper.
Appl. Sci. 2020, 10(18), 6335; https://doi.org/10.3390/app10186335
Received: 20 August 2020 / Revised: 4 September 2020 / Accepted: 7 September 2020 / Published: 11 September 2020
(This article belongs to the Section Earth Sciences and Geography)
To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind. View Full-Text
Keywords: landslide; landslide susceptibility modeling; landslide controlling factors; landslide inventory landslide; landslide susceptibility modeling; landslide controlling factors; landslide inventory
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MDPI and ACS Style

Pawluszek-Filipiak, K.; Oreńczak, N.; Pasternak, M. Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping. Appl. Sci. 2020, 10, 6335. https://doi.org/10.3390/app10186335

AMA Style

Pawluszek-Filipiak K, Oreńczak N, Pasternak M. Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping. Applied Sciences. 2020; 10(18):6335. https://doi.org/10.3390/app10186335

Chicago/Turabian Style

Pawluszek-Filipiak, Kamila; Oreńczak, Natalia; Pasternak, Marta. 2020. "Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping" Appl. Sci. 10, no. 18: 6335. https://doi.org/10.3390/app10186335

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