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Debris flow susceptibility mapping using the Rock Engineering System (RES) method: a case study.

Authors :
Vianello, Davide
Vagnon, Federico
Bonetto, Sabrina
Mosca, Pietro
Source :
Landslides. Apr2023, Vol. 20 Issue 4, p735-756. 22p.
Publication Year :
2023

Abstract

The main purpose of the present study is to develop a debris flow susceptibility map of a mountain area (Susa Valley, Western Italian Alps) by using an upgraded version of the Bonetto et al. (Journal of Mountain Science 18, 2021) approach based on the Rock Engineering System (RES) method. In particular, the area under investigation was discretized in a 5 × 5-m grid on which GIS-based analyses were performed. Starting from available databases, several geological, geo-structural, morphological and hydrographical predisposing parameters were identified and codified into two interaction matrices (one for outcropping lithologies and one for Quaternary deposits), to evaluate their mutual interactions and their weight in the susceptibility estimation. The result for each grid point is the debris flow propensity index (DfPI), an index that estimates the susceptibility of the cell to be a potential debris flow source. The debris flow susceptibility map obtained was compared with those obtained from two expedited and universally recognized susceptibility methods, i.e. the Regional Qualitative Heuristic Susceptibility Mapping (RQHSM) and the Likelihood Ratio (LR). Each map was validated by using the Prediction Rate Curve method. The limitations and strong points of the approaches analysed are discussed, with a focus on the innovativeness and uniqueness of the RES. In fact, in the study site, the RES method was the most efficient for the detection of potential source areas. These results prove its robustness, cost-effectiveness and speed of application in the identification and mapping of sectors capable of triggering debris flow. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1612510X
Volume :
20
Issue :
4
Database :
Academic Search Index
Journal :
Landslides
Publication Type :
Academic Journal
Accession number :
162587874
Full Text :
https://doi.org/10.1007/s10346-022-01985-6