Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

Handle URI:
http://hdl.handle.net/10754/626537
Title:
Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster
Authors:
Lombardo, Luigi ( 0000-0003-4348-7288 ) ; Opitz, Thomas; Huser, Raphaël ( 0000-0002-1228-2071 )
Abstract:
We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support on the unobserved rainfall pattern.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Citation:
Lombardo L, Opitz T, Huser R (2018) Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster. Stochastic Environmental Research and Risk Assessment. Available: http://dx.doi.org/10.1007/s00477-018-1518-0.
Publisher:
Springer Nature
Journal:
Stochastic Environmental Research and Risk Assessment
Issue Date:
13-Feb-2018
DOI:
10.1007/s00477-018-1518-0
ARXIV:
arXiv:1708.03156
Type:
Article
ISSN:
1436-3240; 1436-3259
Sponsors:
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 modelling: application in Mediterranean and tropical Pacific areas. Principal Investigator: Dr. Luigi Lombardo.
Additional Links:
https://link.springer.com/article/10.1007%2Fs00477-018-1518-0; http://arxiv.org/abs/1708.03156v1
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLombardo, Luigien
dc.contributor.authorOpitz, Thomasen
dc.contributor.authorHuser, Raphaëlen
dc.date.accessioned2018-02-15T10:00:59Z-
dc.date.available2017-12-28T07:32:15Z-
dc.date.available2018-02-15T10:00:59Z-
dc.date.issued2018-02-13en
dc.identifier.citationLombardo L, Opitz T, Huser R (2018) Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster. Stochastic Environmental Research and Risk Assessment. Available: http://dx.doi.org/10.1007/s00477-018-1518-0.en
dc.identifier.issn1436-3240en
dc.identifier.issn1436-3259en
dc.identifier.doi10.1007/s00477-018-1518-0en
dc.identifier.urihttp://hdl.handle.net/10754/626537-
dc.description.abstractWe develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support on the unobserved rainfall pattern.en
dc.description.sponsorshipPart 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 modelling: application in Mediterranean and tropical Pacific areas. Principal Investigator: Dr. Luigi Lombardo.en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.urlhttps://link.springer.com/article/10.1007%2Fs00477-018-1518-0en
dc.relation.urlhttp://arxiv.org/abs/1708.03156v1-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s00477-018-1518-0en
dc.subjectIntegrated nested Laplace approximationen
dc.subjectLandslide susceptibilityen
dc.subjectLog-Gaussian Cox processen
dc.subjectMapping unitsen
dc.subjectSpatial point patternen
dc.titlePoint process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disasteren
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalStochastic Environmental Research and Risk Assessmenten
dc.eprint.versionPost-printen
dc.contributor.institutionUR546 Biostatistics and Spatial Processes, INRA, Avignon, Franceen
dc.identifier.arxividarXiv:1708.03156-
kaust.authorLombardo, Luigien
kaust.authorHuser, Raphaëlen

Version History

VersionItem Editor Date Summary
2 10754/626537grenzdm2018-02-15 09:59:56.943Published in journal with DOI.
1 10754/626537.1grenzdm2017-12-28 07:32:15.0
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