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Prospective océan-atmosphère 2023-2028

dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire Interuniversitaire des Systèmes Atmosphériques [LISA (UMR_7583)]
hal.structure.identifierInstitut national des sciences de l'Univers [INSU - CNRS]
dc.contributor.authorDOUSSIN, Jean-Francois
hal.structure.identifierCentre Européen de Recherche et d'Enseignement des Géosciences de l'Environnement [CEREGE]
dc.contributor.authorDE GARIDEL-THORON, Thibault
hal.structure.identifierLaboratoire d'Océanographie Physique et Spatiale [LOPS]
dc.contributor.authorLHERMINIER, P.
hal.structure.identifierLaboratoire de Météorologie Dynamique (UMR 8539) [LMD]
dc.contributor.authorBONY, S.
hal.structure.identifierTROPO - LATMOS
dc.contributor.authorPAYAN, Sébastien
hal.structure.identifierTROPO - LATMOS
dc.contributor.authorFLAMANT, Cyrille
hal.structure.identifierProcessus et interactions de fine échelle océanique [LOCEAN-PROTEO]
dc.contributor.authorCAPET, Xavier
hal.structure.identifierLaboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] [LSCE]
hal.structure.identifierChimie Atmosphérique Expérimentale [CAE]
dc.contributor.authorCHAZETTE, P.
hal.structure.identifierLaboratoire d'aérologie [LAERO]
dc.contributor.authorDELON, C.
hal.structure.identifierCentre national de recherches météorologiques [CNRM]
dc.contributor.authorCOUVREUX, Fleur
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorDURAND, G.
hal.structure.identifierLaboratoire de Météorologie Physique [LaMP]
dc.contributor.authorFRENEY, E.
hal.structure.identifierTROPO - LATMOS
dc.contributor.authorLAW, Kathy S.
hal.structure.identifierOcéan du Large et Variabilité Climatique [OLVAC]
dc.contributor.authorMORROW, R.
hal.structure.identifierCentre Européen de Recherche et de Formation Avancée en Calcul Scientifique [CERFACS]
dc.contributor.authorSANCHEZ-GOMEZ, E.
hal.structure.identifierLaboratoire d’Optique Atmosphérique - UMR 8518 [LOA]
dc.contributor.authorRIÉDI, J.
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorSAVOYE, Nicolas
hal.structure.identifierLaboratoire d'océanographie de Villefranche [LOV]
dc.contributor.authorD'ORTENZIO, F.
hal.structure.identifierLaboratoire d'aérologie [LAERO]
dc.contributor.authorLOHOU, F.
hal.structure.identifierLaboratoire Imagerie et Systèmes d'Acquisition [LISA]
dc.contributor.authorFORMENTI, Paola
hal.structure.identifierLaboratoire Imagerie et Systèmes d'Acquisition [LISA]
dc.contributor.authorFORÊT, G.
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorUZU, G.
hal.structure.identifierLaboratoire d'aérologie [LAERO]
dc.contributor.authorLÉON, J. F.
hal.structure.identifierCentre national de recherches météorologiques [CNRM]
dc.contributor.authorMORIN, Samuel
hal.structure.identifierLaboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] [LSCE]
hal.structure.identifierModélisation INVerse pour les mesures atmosphériques et SATellitaires [SATINV]
dc.contributor.authorBOUSQUET, Philippe
hal.structure.identifierLaboratoire Imagerie et Systèmes d'Acquisition [LISA]
dc.contributor.authorCIRTOG, M.
hal.structure.identifierCentre national de recherches météorologiques [CNRM]
dc.contributor.authorMARÉCAL, Virginie
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorSAVARINO, J.
hal.structure.identifierLaboratoire Interuniversitaire des Systèmes Atmosphériques [LISA (UMR_7583)]
dc.contributor.authorDUFOUR, Gaelle
hal.structure.identifierInstitut méditerranéen d'océanologie [MIO]
dc.contributor.authorWAGENER, Thibaut
hal.structure.identifierCentre for Energy and Environment [CERI EE - IMT Nord Europe]
dc.contributor.authorSAUVAGE, Stephane
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorVILLENAVE, Eric
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorSAVARINO, Joël
hal.structure.identifierInstitut méditerranéen d'océanologie [MIO]
dc.contributor.authorTAMBURINI, Christian
hal.structure.identifierLaboratoire d'océanographie de Villefranche [LOV]
dc.contributor.authorD'ORTENZIO, Fabrizio
hal.structure.identifierLaboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
hal.structure.identifierUniversité du Littoral Côte d'Opale [ULCO]
dc.contributor.authorCHRISTAKI, Urania
dc.date.accessioned2024-03-13T11:06:04Z
dc.date.available2024-03-13T11:06:04Z
dc.date.issued2023-09-30
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/188778
dc.description.abstractEnBackground: Southwest France golden-sand beaches are very popular destination for bathing and other sea activities in summer. However, they are also potentially dangerous environments with increased risk of accidents in unsupervised areas, especially during the off-peak season, due to strong rip-current and shorebreak waves. Predicting and quantifying these accidents is of major importance for public communication and emergency services management. Previous work on beach risk prediction was conducted along a specific section of the coast (Gironde), using data from 2011-2017 to train a model and further predict drowning incidents based on sea and weather forecasts, which has led to the development of an alert system based on a logistic regression model used by local decision makers [1]. Methods: In this study, we further improve this model by using new statistical methods related to machine learning, a larger dataset (2011-2022) and by including spatialization in order to propose a modelling framework that could be generalized to other coasts. We estimated drowning risk as a combination of hazard (ocean conditions) and exposure (beachgoer crowd). Several machine learning models were trained and compared using 3-day weather and sea forecasts from 2011 to 2022 as predictors along with an emergency calls database used as an outcome on the same time frame. The training set covered 188 drowning events over 1988 days while the test set covered 81 events over 663 days. Results: Our results show this new modeling framework is able to predict days with the highest risk of drowning events with improved accuracy on the Gironde coast: AUC = 0.9 (95% CI 0.89 to 0.91), PPV = 0.49 (95%CI 0.41 to 0.55) and NPV = 0.96 (95%CI 0.95 to 0.99). Conclusions: This supports the development of a new alert system that will provide useful information to decision makers. However, “all models are wrong, but some are useful” [2]. While this model could still be improved, with further feature engineering and improved data for rescues, this work also addresses the issue of identifying the right criteria to define what would actually be the “best” model depending on risk management policies set by decision makers.
dc.language.isoENen_US
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.endecision trees
dc.subject.enprediction
dc.subject.enrisk
dc.subject.enbeach safety
dc.subject.endrowning
dc.subject.enFrance
dc.subject.enMachine learning
dc.titleProspective océan-atmosphère 2023-2028
dc.title.enSynthèse des ateliers du colloque de synthèse de prospective du domaine océan-atmosphère (OA) des 10-13 janvier 2023 à Autrans et des groupes de travail préparatoires
dc.typeRapporten_US
dc.subject.halPlanète et Univers [physics]en_US
dc.subject.halSciences de l'environnementen_US
bordeaux.hal.laboratoriesEPOC : Environnements et Paléoenvironnements Océaniques et Continentaux - UMR 5805en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.type.institutionInstitut National des Sciences de l'Universen_US
bordeaux.teamECOBIOCen_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-04334125
hal.version1
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccCC BYen_US
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