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dc.contributor.authorLombardo, Luigi
dc.contributor.authorMai, Paul Martin
dc.date.accessioned2018-09-26T13:27:18Z
dc.date.available2018-09-26T13:27:18Z
dc.date.issued2018-07-24
dc.identifier.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.
dc.identifier.issn0013-7952
dc.identifier.doi10.1016/j.enggeo.2018.07.019
dc.identifier.urihttp://hdl.handle.net/10754/628758
dc.description.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.
dc.description.sponsorshipThe 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.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0013795218301212
dc.rightsUnder a Creative Commons license
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBinary logistic regression
dc.subjectLandslide susceptibility
dc.subjectStandardized results
dc.subjectLeast Absolute Shrinkage Selection Operator (LASSO)
dc.titlePresenting logistic regression-based landslide susceptibility results
dc.typeArticle
dc.contributor.departmentComputational Earthquake Seismology (CES) Research Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalEngineering Geology
dc.eprint.versionPublisher's Version/PDF
kaust.personLombardo, Luigi
kaust.personMai, Paul Martin
refterms.dateFOA2018-09-27T08:48:59Z
dc.date.published-online2018-07-24
dc.date.published-print2018-10


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