Presenting logistic regression-based landslide susceptibility results
Type
ArticleAuthors
Lombardo, LuigiMai, Paul Martin

KAUST Department
Computational Earthquake Seismology (CES) Research GroupComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2018-07-24Online Publication Date
2018-07-24Print Publication Date
2018-10Permanent link to this record
http://hdl.handle.net/10754/628758
Metadata
Show full item recordAbstract
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.Citation
Lombardo 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.Sponsors
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.Publisher
Elsevier BVJournal
Engineering GeologyAdditional Links
http://www.sciencedirect.com/science/article/pii/S0013795218301212ae974a485f413a2113503eed53cd6c53
10.1016/j.enggeo.2018.07.019