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dc.contributor.authorBordoni, M.
dc.contributor.authorGalve Arnedo, Jorge Pedro 
dc.date.accessioned2021-02-12T08:35:40Z
dc.date.available2021-02-12T08:35:40Z
dc.date.issued2020-12-03
dc.identifier.citationBordoni, M., Vivaldi, V., Lucchelli, L. et al. Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale. Landslides (2020). [https://doi.org/10.1007/s10346-020-01592-3]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/66487
dc.descriptionOpen access funding provided by Universita degli Studi di Pavia within the CRUI-CARE Agreement. This work has been in the frame of the ANDROMEDA project, which has been supported by Fondazione Cariplo, grant no. 2017-0677.es_ES
dc.descriptionWe thank the anonymous reviewers for their contributions in improving the paper. We thank Beatrice Corradini for the help in the collection of rainfall data and of shallow landslide events.es_ES
dc.description.abstractA combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression Splines technique, was based on a joint probability between the spatial probability of occurrence (susceptibility) and the temporal one. The former was estimated on the basis of geological, geomorphological, and hydrological predictors. The latter was assessed considering short-term cumulative rainfall, antecedent rainfall, soil hydrological conditions, expressed as soil saturation degree, and bedrock geology. The predictive capability of the methodology was tested for past triggering events of shallow landslides occurred in representative catchments of Oltrepò Pavese, in northern Italian Apennines. The method provided excellently to outstanding performance for both the really unstable hillslopes (area under ROC curve until 0.92, true positives until 98.8%, true negatives higher than 80%) and the identification of the triggering time (area under ROC curve of 0.98, true positives of 96.2%, true negatives of 94.6%). The developed methodology allowed us to obtain feasible results using satellite-based rainfall products and data acquired by field rain gauges. Advantages and weak points of the method, in comparison also with traditional approaches for the forecast of shallow landslides, were also provided.es_ES
dc.description.sponsorshipUniversita degli Studi di Pavia within the CRUI-CARE Agreementes_ES
dc.description.sponsorshipFondazione Cariplo 2017-0677es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectShallow landslideses_ES
dc.subjectData-driven methodses_ES
dc.subjectRainfalles_ES
dc.subjectSoil saturation degreees_ES
dc.subjectRemote sensing es_ES
dc.titleDevelopment of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scalees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1007/s10346-020-01592-3
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España