Presenting logistic regression-based landslide susceptibility results

By Luigi Lombardo, P. M. Mai. Engineering Geology, 2018

237 citations recorded by the source corpus.

Abstract A new work-flow is proposed to unify the way the community shares Logistic Regression results for landslide susceptibility purposes. Although Logistic Regression models and methods have been widely used in geomorphology for several decades, no standards for presenting results in a consistent way have been adopted; most papers report parameters with different units and interpretations, therefore limiting potential meta-analytic applications. We first summarize the major differences in the geomorphological literature and then investigate each one proposing current best practices and few methodological developments. The latter is mainly represented by a widely used approach in statistics for simultaneous parameter estimation and variable selection in generalized linear models, namely the Least Absolute Shrinkage Selection Operator (LASSO). The North-easternmost sector of Sicily (Italy) is chosen as a straightforward example with well exposed debris flows induced by extreme rainfall.

Subfields: Civil and Structural Engineering, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/9f33d77c9a0274d5a039384cf976415274d4e0bc

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Citations in the corpus

Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment (2019, methodology): “For a more extensive361 explanation on LASSO and how to compute λ, we refer to Lombardo and Mai (2018) for362 a more geoscientific readership and to Tibshirani (1996) for a more statistically-oriented363 audience.364 In the context of the present paper, we used a LASSO penalized Binary Logistic…”
Space-time landslide predictive modelling (2019, methodology): “2018). A number of authors have used quantiles to segment the continuous probability estimates into discrete susceptibility classes, e.g., from \low" to \high" susceptibility. As an example,Lombardo and Mai (2018) used 2:5, 25, 50, 75, and 97:5 percentiles (i.e., where the three central va”