Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks

National and international technical reports have demonstrated the increase of extreme event occurrences which becomes more dangerous in coastal areas due to their higher population density. In Spain, flood and storm events are the main reasons for compensation according to the National Insurance Co...

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Detalles Bibliográficos
Autores: Flores, M. Julia, Ropero, Rosa, F., Rumi, Rafael
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/28303
Acceso en línea:http://hdl.handle.net/10578/28303
Access Level:acceso abierto
Palabra clave:Flood risk assessment
Dynamic Bayesian networks
Object Oriented Bayesian networks
Mediterranean watershed
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spelling Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian NetworksFlores, M. JuliaRopero, Rosa, F.Rumi, RafaelFlood risk assessmentDynamic Bayesian networksObject Oriented Bayesian networksMediterranean watershedNational and international technical reports have demonstrated the increase of extreme event occurrences which becomes more dangerous in coastal areas due to their higher population density. In Spain, flood and storm events are the main reasons for compensation according to the National Insurance Consortium. The aim of this paper is to model the risk of flooding in a Mediterranean catchment in the South of Spain. A hybrid dynamic Object Oriented Bayesian network was learnt based on Mixture of Truncated Exponential models, a scenario of rainfall event was included and the final model validated. OOBN structure allows the catchment to be divided into 5 different units and models each of them independently. It transforms a complex problem into a simple and easily interpretable model. Results show that the model is able to accurately watch the evolution of river level, by predicting its increase and the time the river needs to recover normality, which can be defined as the river resilienceSpringer202120212019info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttp://hdl.handle.net/10578/28303reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/283032026-05-27T07:36:41Z
dc.title.none.fl_str_mv Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks
title Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks
spellingShingle Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks
Flores, M. Julia
Flood risk assessment
Dynamic Bayesian networks
Object Oriented Bayesian networks
Mediterranean watershed
title_short Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks
title_full Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks
title_fullStr Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks
title_full_unstemmed Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks
title_sort Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks
dc.creator.none.fl_str_mv Flores, M. Julia
Ropero, Rosa, F.
Rumi, Rafael
author Flores, M. Julia
author_facet Flores, M. Julia
Ropero, Rosa, F.
Rumi, Rafael
author_role author
author2 Ropero, Rosa, F.
Rumi, Rafael
author2_role author
author
dc.subject.none.fl_str_mv Flood risk assessment
Dynamic Bayesian networks
Object Oriented Bayesian networks
Mediterranean watershed
topic Flood risk assessment
Dynamic Bayesian networks
Object Oriented Bayesian networks
Mediterranean watershed
description National and international technical reports have demonstrated the increase of extreme event occurrences which becomes more dangerous in coastal areas due to their higher population density. In Spain, flood and storm events are the main reasons for compensation according to the National Insurance Consortium. The aim of this paper is to model the risk of flooding in a Mediterranean catchment in the South of Spain. A hybrid dynamic Object Oriented Bayesian network was learnt based on Mixture of Truncated Exponential models, a scenario of rainfall event was included and the final model validated. OOBN structure allows the catchment to be divided into 5 different units and models each of them independently. It transforms a complex problem into a simple and easily interpretable model. Results show that the model is able to accurately watch the evolution of river level, by predicting its increase and the time the river needs to recover normality, which can be defined as the river resilience
publishDate 2019
dc.date.none.fl_str_mv 2019
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10578/28303
url http://hdl.handle.net/10578/28303
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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