Presenting logistic regression-based landslide susceptibility results

Luigi Lombardo, Paul Martin Mai

Research output: Contribution to journalArticlepeer-review

108 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)14-24
Number of pages11
JournalEngineering Geology
Volume244
DOIs
StatePublished - Jul 24 2018

Bibliographical note

KAUST Repository Item: Exported on 2021-02-19
Acknowledgements: The authors would like to thank Dr. Daniela Castro Camilo as the code used throughout the analyses is a slight modification of the LUDARA code included in Castro Camilo et al. (2017). Part of the satellite images used to generate the landslide inventory were obtained thanks to the European Space Agency Project (ID: 14151) titled: A remote sensing based approach for storm triggered debris flow hazard modeling: application in Mediterranean and tropical Pacific areas. Principal Investigator: Dr. Luigi Lombardo.

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