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Uncertainty Quantification of Contaminated Soil Volume with Deep Neural Networks and Predictive Models

dc.rights.licenseopenen_US
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorGURIDI, Ignacio
hal.structure.identifierBureau de Recherches Géologiques et Minières [BRGM]
dc.contributor.authorCHASSAGNE, Romain
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorPRYET, Alexandre
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorATTEIA, Olivier
IDREF: 078590272
dc.date.accessioned2024-02-14T08:38:38Z
dc.date.available2024-02-14T08:38:38Z
dc.date.issued2023-08-24
dc.identifier.issn1420-2026en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/188114
dc.description.abstractEnThe estimation of the soil volume exceeding a contamination threshold over decommissioned industrial sites is critical for the design of remediation strategies. In practice, the volume calculation is mostly based on preliminary sampling surveys and the use of interpolation methods. However, if the volume is not estimated correctly, this can lead to environmental and economic risks. Geostatistical-oriented methodologies have been developed to better assess the volume using uncertainty ranges. In our study, we propose a methodology entitled “Evol” to better estimate the volume and reduce the uncertainty ranges with a combination of classic non-parametrical interpolation techniques and deep learning. Evol consists of generating a synthetic model from a real polluted site, extracting descriptive variables (features) from multiple sample sets, and evaluating the error in the volume calculation. A Deep Neural Network model is then trained with the features to estimate the volume and uncertainty range for any sample set. Our methodology demonstrated high accuracy in error estimation, as evidenced by a low RMSE of 0.008 across most sample sets. Additionally, the confidence volume intervals produced by our approach were narrower than those generated by classic techniques, resulting in interval size reductions of up to 89%.
dc.language.isoENen_US
dc.title.enUncertainty Quantification of Contaminated Soil Volume with Deep Neural Networks and Predictive Models
dc.typeArticle de revueen_US
dc.identifier.doi10.1007/s10666-023-09924-yen_US
dc.subject.halPlanète et Univers [physics]/Sciences de la Terreen_US
bordeaux.journalEnvironmental Modeling & Assessmenten_US
bordeaux.hal.laboratoriesEPOC : Environnements et Paléoenvironnements Océaniques et Continentaux - UMR 5805en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.teamPROMESSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-04191490
hal.version1
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Environmental%20Modeling%20&%20Assessment&rft.date=2023-08-24&rft.eissn=1420-2026&rft.issn=1420-2026&rft.au=GURIDI,%20Ignacio&CHASSAGNE,%20Romain&PRYET,%20Alexandre&ATTEIA,%20Olivier&rft.genre=article


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