Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

Handle URI:
http://hdl.handle.net/10754/626008
Title:
Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model
Authors:
Camilo, Daniela Castro; Lombardo, Luigi; Mai, Paul Martin ( 0000-0002-9744-4964 ) ; Dou, Jie ( 0000-0001-5930-199X ) ; Huser, Raphaël ( 0000-0002-1228-2071 )
Abstract:
Grid-based landslide susceptibility models at regional scales are computationally demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based susceptibility models allows to investigate the same areas offering two main advantages: 1) a smaller computational burden and 2) a more geomorphologically-oriented interpretation. In this contribution, we generate SU-based landslide susceptibility for the Sado Island in Japan. This island is characterized by deep-seated landslides which we assume can only limitedly be explained by the first two statistical moments (mean and variance) of a set of predictors within each slope unit. As a consequence, in a nested experiment, we first analyse the distributions of a set of continuous predictors within each slope unit computing the standard deviation and quantiles from 0.05 to 0.95 with a step of 0.05. These are then used as predictors for landslide susceptibility. In addition, we combine shape indices for polygon features and the normalized extent of each class belonging to the outcropping lithology in a given SU. This procedure significantly enlarges the size of the predictor hyperspace, thus producing a high level of slope-unit characterization. In a second step, we adopt a LASSO-penalized Generalized Linear Model to shrink back the predictor set to a sensible and interpretable number, carrying only the most significant covariates in the models. As a result, we are able to document the geomorphic features (e.g., 95% quantile of Elevation and 5% quantile of Plan Curvature) that primarily control the SU-based susceptibility within the test area while producing high predictive performances. The implementation of the statistical analyses are included in a parallelized R script (LUDARA) which is here made available for the community to replicate analogous experiments.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Citation:
Camilo DC, Lombardo L, Mai PM, Dou J, Huser R (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. Environmental Modelling & Software 97: 145–156. Available: http://dx.doi.org/10.1016/j.envsoft.2017.08.003.
Publisher:
Elsevier BV
Journal:
Environmental Modelling & Software
Issue Date:
30-Aug-2017
DOI:
10.1016/j.envsoft.2017.08.003
Type:
Article
ISSN:
1364-8152
Additional Links:
http://www.sciencedirect.com/science/article/pii/S1364815216311203
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCamilo, Daniela Castroen
dc.contributor.authorLombardo, Luigien
dc.contributor.authorMai, Paul Martinen
dc.contributor.authorDou, Jieen
dc.contributor.authorHuser, Raphaëlen
dc.date.accessioned2017-10-30T08:39:50Z-
dc.date.available2017-10-30T08:39:50Z-
dc.date.issued2017-08-30en
dc.identifier.citationCamilo DC, Lombardo L, Mai PM, Dou J, Huser R (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. Environmental Modelling & Software 97: 145–156. Available: http://dx.doi.org/10.1016/j.envsoft.2017.08.003.en
dc.identifier.issn1364-8152en
dc.identifier.doi10.1016/j.envsoft.2017.08.003en
dc.identifier.urihttp://hdl.handle.net/10754/626008-
dc.description.abstractGrid-based landslide susceptibility models at regional scales are computationally demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based susceptibility models allows to investigate the same areas offering two main advantages: 1) a smaller computational burden and 2) a more geomorphologically-oriented interpretation. In this contribution, we generate SU-based landslide susceptibility for the Sado Island in Japan. This island is characterized by deep-seated landslides which we assume can only limitedly be explained by the first two statistical moments (mean and variance) of a set of predictors within each slope unit. As a consequence, in a nested experiment, we first analyse the distributions of a set of continuous predictors within each slope unit computing the standard deviation and quantiles from 0.05 to 0.95 with a step of 0.05. These are then used as predictors for landslide susceptibility. In addition, we combine shape indices for polygon features and the normalized extent of each class belonging to the outcropping lithology in a given SU. This procedure significantly enlarges the size of the predictor hyperspace, thus producing a high level of slope-unit characterization. In a second step, we adopt a LASSO-penalized Generalized Linear Model to shrink back the predictor set to a sensible and interpretable number, carrying only the most significant covariates in the models. As a result, we are able to document the geomorphic features (e.g., 95% quantile of Elevation and 5% quantile of Plan Curvature) that primarily control the SU-based susceptibility within the test area while producing high predictive performances. The implementation of the statistical analyses are included in a parallelized R script (LUDARA) which is here made available for the community to replicate analogous experiments.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S1364815216311203en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software, [97, , (2017-08-30)] DOI: 10.1016/j.envsoft.2017.08.003 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectHigh predictor dimensionalityen
dc.subjectLasso penalized variable selectionen
dc.subjectOpen sourcingen
dc.subjectR scriptingen
dc.subjectSlope Unit-based landslide susceptibilityen
dc.titleHandling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Modelen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalEnvironmental Modelling & Softwareen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Geosciences, Tübingen University, Tübingen, Germanyen
dc.contributor.institutionPublic Works Research Institute, Japanen
kaust.authorCamilo, Daniela Castroen
kaust.authorLombardo, Luigien
kaust.authorMai, Paul Martinen
kaust.authorHuser, Raphaëlen
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