Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks

Natural disasters, especially those related to water—like storms and floods—have increased over the last decades both in number and intensity. Under the current Climate Change framework, several reports predict an increase in the intensity and duration of these extreme climatic events, where the Med...

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Detalles Bibliográficos
Autores: Ropero, Rosa F., Flores Gallego, María Julia, Rumí, Rafael
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/45706
Acceso en línea:https://doi.org/10.1007/s10651-023-00587-2
https://link.springer.com/article/10.1007/s10651-023-00587-2
https://hdl.handle.net/10578/45706
Access Level:acceso abierto
Palabra clave:Bayesian networks
Decision support system
Flood risk
Regression model
Rule-based meta-model
Unsupervised classification
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spelling Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networksRopero, Rosa F.Flores Gallego, María JuliaRumí, RafaelBayesian networksDecision support systemFlood riskRegression modelRule-based meta-modelUnsupervised classificationNatural disasters, especially those related to water—like storms and floods—have increased over the last decades both in number and intensity. Under the current Climate Change framework, several reports predict an increase in the intensity and duration of these extreme climatic events, where the Mediterranean area would be one of the most affected. This paper develops a decision support system based on Bayesian inference able to predict a flood alert in Andalusian Mediterranean catchments. The key point is that, using simple weather forecasts and live measurements of river level, we can get a flood-alert several hours before it happens. A set of models based on Bayesian networks was learnt for each of the catchments included in the study area, and joined together into a more complex model based on a rule system. This final meta-model was validated using data from both non-extreme and extreme storm events. Results show that the methodology proposed provides an accurate forecast of the flood situation of the greatest catchment areas of Andalusia.Springer202520252024info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1007/s10651-023-00587-2https://link.springer.com/article/10.1007/s10651-023-00587-2https://hdl.handle.net/10578/45706reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésSAICMA (UAL18-TIC-A011-B-E)PID2022-139293NB-C31PID2022-139293NB-C32PID2019-106758GB-C32/AEI/10PID2019-106758GB-C33info:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/457062026-05-27T07:36:41Z
dc.title.none.fl_str_mv Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
title Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
spellingShingle Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
Ropero, Rosa F.
Bayesian networks
Decision support system
Flood risk
Regression model
Rule-based meta-model
Unsupervised classification
title_short Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
title_full Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
title_fullStr Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
title_full_unstemmed Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
title_sort Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
dc.creator.none.fl_str_mv Ropero, Rosa F.
Flores Gallego, María Julia
Rumí, Rafael
author Ropero, Rosa F.
author_facet Ropero, Rosa F.
Flores Gallego, María Julia
Rumí, Rafael
author_role author
author2 Flores Gallego, María Julia
Rumí, Rafael
author2_role author
author
dc.subject.none.fl_str_mv Bayesian networks
Decision support system
Flood risk
Regression model
Rule-based meta-model
Unsupervised classification
topic Bayesian networks
Decision support system
Flood risk
Regression model
Rule-based meta-model
Unsupervised classification
description Natural disasters, especially those related to water—like storms and floods—have increased over the last decades both in number and intensity. Under the current Climate Change framework, several reports predict an increase in the intensity and duration of these extreme climatic events, where the Mediterranean area would be one of the most affected. This paper develops a decision support system based on Bayesian inference able to predict a flood alert in Andalusian Mediterranean catchments. The key point is that, using simple weather forecasts and live measurements of river level, we can get a flood-alert several hours before it happens. A set of models based on Bayesian networks was learnt for each of the catchments included in the study area, and joined together into a more complex model based on a rule system. This final meta-model was validated using data from both non-extreme and extreme storm events. Results show that the methodology proposed provides an accurate forecast of the flood situation of the greatest catchment areas of Andalusia.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1007/s10651-023-00587-2
https://link.springer.com/article/10.1007/s10651-023-00587-2
https://hdl.handle.net/10578/45706
url https://doi.org/10.1007/s10651-023-00587-2
https://link.springer.com/article/10.1007/s10651-023-00587-2
https://hdl.handle.net/10578/45706
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv SAICMA (UAL18-TIC-A011-B-E)
PID2022-139293NB-C31
PID2022-139293NB-C32
PID2019-106758GB-C32/AEI/10
PID2019-106758GB-C33
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 15,812429

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