Presenting logistic regression-based landslide susceptibility results
KAUST DepartmentComputational Earthquake Seismology (CES) Research Group
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2018-07-24
Print Publication Date2018-10
Permanent link to this recordhttp://hdl.handle.net/10754/628758
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AbstractA 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.
CitationLombardo L, Mai PM (2018) Presenting logistic regression-based landslide susceptibility results. Engineering Geology 244: 14–24. Available: http://dx.doi.org/10.1016/j.enggeo.2018.07.019.
SponsorsThe 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.