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LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation

Articolo
Data di Pubblicazione:
2022
Abstract:
In the past 50 years, a large variety of statistically based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, which are applicable to a large range of spatial scales, use a large variety of input thematic data, different model combinations, and several approaches to evaluate the models' performance. Despite the numerous applications available in the literature, a standard approach for susceptibility modeling and zonation is still missing. The literature search revealed that several software program and tools are available to evaluate regional slope stability using physically based analysis, but only a few use statistically based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possibility to perform and combine different statistical susceptibility models and to evaluate their performances and associated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for statistically based landslide susceptibility modeling which integrates LAND-SE. LAND-SUITE completes and extends LAND-SE, adding functionalities to (i) facilitate input data preparation, (ii) perform preliminary and exploratory analysis of the available data, and (iii) test different combinations of variables and select the optimal thematic/explanatory set. LAND-SUITE provides a tool to assist the user during the data preparatory phase and to perform diversified statistically based landslide susceptibility applications.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
software; susceptibility; landslide; zonation
Elenco autori:
Bornaetxea, Txomin; Reichenbach, Paola; Rossi, Mauro
Autori di Ateneo:
REICHENBACH PAOLA
ROSSI MAURO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/463778
Pubblicato in:
GEOSCIENTIFIC MODEL DEVELOPMENT (PRINT)
Journal
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URL

https://gmd.copernicus.org/articles/15/5651/2022/
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