A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan
Abstract Kyrgyzstan is one of the most exposed countries in the world to landslide hazard. The large variability of local geological materials, together with the difficulties in forecasting heavy precipitation locally and in quantifying the level of ground shaking, call for harmonized procedures to better quantify the hazard and the negative impact of slope failures. By exploiting new advances in Geographic Information System (GIS) technology, together with concepts from Bayesian statistics, and promoting the use of open-source tools, we aim to identify areas in Kyrgyzstan where the potential for landslide activation exists. A range of conditioning factors and their potential impact on landslide occurrence are quantitatively assessed on the basis of the spatial distribution of landslides by applying weights-of-evidence modelling based on (1) a landslide inventory of past events, (2) terrain-derived variables of slope, aspect and curvature, (3) a geological map, (4) a distance from faults map, and (5) a seismic intensity map. A spatial validation of the proposed method has been performed, indicating sufficient measures of significance to predicted results. Initial results are promising and demonstrate the applicability of the method to all of Kyrgyzstan, allowing the identification of areas that are more susceptible to landslides with a level of accuracy greater than 70 %. The presented method is, therefore, capable of supporting land planning activities at the regional scale in places where only scarce data are available.
Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan
Abstract Kyrgyzstan is one of the most exposed countries in the world to landslide hazard. The large variability of local geological materials, together with the difficulties in forecasting heavy precipitation locally and in quantifying the level of ground shaking, call for harmonized procedures to better quantify the hazard and the negative impact of slope failures. By exploiting new advances in Geographic Information System (GIS) technology, together with concepts from Bayesian statistics, and promoting the use of open-source tools, we aim to identify areas in Kyrgyzstan where the potential for landslide activation exists. A range of conditioning factors and their potential impact on landslide occurrence are quantitatively assessed on the basis of the spatial distribution of landslides by applying weights-of-evidence modelling based on (1) a landslide inventory of past events, (2) terrain-derived variables of slope, aspect and curvature, (3) a geological map, (4) a distance from faults map, and (5) a seismic intensity map. A spatial validation of the proposed method has been performed, indicating sufficient measures of significance to predicted results. Initial results are promising and demonstrate the applicability of the method to all of Kyrgyzstan, allowing the identification of areas that are more susceptible to landslides with a level of accuracy greater than 70 %. The presented method is, therefore, capable of supporting land planning activities at the regional scale in places where only scarce data are available.
Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan
Saponaro, Annamaria (author) / Pilz, Marco (author) / Wieland, Marc (author) / Bindi, Dino (author) / Moldobekov, Bolot (author) / Parolai, Stefano (author)
2014
Article (Journal)
English
Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan
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