Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment

By Gabriele Amato, Clemens Eisank, Daniela Castro-Camilo, Luigi Lombardo. Engineering Geology, 2019

67 citations recorded by the source corpus.

Abstract Thousands or even million of pixels can be contained in a single Slope Unit. Hence, each covariate used in spatial predictive models is characterized by a distribution of values for each Slope Unit. Here, we model the whole covariates' distribution within Slope Units for landslide susceptibility purposes. This is done by finely dissecting each covariate into quantiles and then modeling the susceptibility via a LASSO penalized Binary Logistic Regression. We choose a LASSO penalization because the common Stepwise procedure is not selective enough to shrink a large number of covariates to an interpretable subset (which we also demonstrate here). LASSO mostly selects 6 covariates out of 372 to explain the spatial distribution of shallow landslides in the Upper Badia valley, Italian Alps. This allows us to verify that the selection does not include any quantile close to the median hence, nor to the mean. The latter is the common representation of the covariates' distribution within Slope Units, which we also test and report in the supplements. Overall, we suggest to always investigate the whole distribution because the mean may not be the most informative nor the most performing way to generate Slope-Unit-based susceptibility models. In this general context, we generate our landslide inventory by combining semi-automated (OBIA) and manual mapping procedures. Our inventory, quantile covariates' representation and LASSO penalization produce excellent performances and interpretable relations between covariates and landslides.

Subfields: Atmospheric Science, Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/a2917bba25e0a7aafa9e4f523c39e670f4773587

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Thomas Opitz
2. Raphaël Huser
3. Luigi Lombardo

In Global and Planetary Change

1. Thomas Opitz
2. Raphaël Huser
3. Fausto Guzzetti
4. A. C. Davison
5. Emeric Thibaud
6. Marc G. Genton

In Management, Monitoring, Policy and Law

1. Thomas Opitz
2. Francesca Ardizzone
3. Raphaël Huser
4. Fausto Guzzetti
5. Luigi Lombardo

Citations in the corpus

• Space-time landslide predictive modelling (2019, background): “, 2003), and in different (Van Den Eeckhaut et al., 2009; Erener and Düzgün, 2012; Castro Camilo et al., 2017; Amato et al., 2019) areas, we select the “slope unit” (SU) as the mapping unit of reference.”