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dc.contributor.authorDehghan-Souraki, Danial
dc.contributor.authorLópez Gómez, David
dc.contributor.authorBladé i Castellet, Ernest
dc.contributor.authorLarese De Tetto, Antonia
dc.contributor.authorSanz Ramos, Marcos
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Civil
dc.date.accessioned2024-03-14T14:40:37Z
dc.date.available2024-03-14T14:40:37Z
dc.date.issued2024-02
dc.identifier.citationDehghan-Souraki, D. [et al.]. Optimizing sediment transport models by using the Monte Carlo simulation and deep neural network (DNN): A case study of the Riba-Roja reservoir. "Environmental modelling & software", Febrer 2024, núm. Article 105979.
dc.identifier.issn1364-8152
dc.identifier.urihttp://hdl.handle.net/2117/404630
dc.description.abstractThis study emphasizes the importance of accurate calibration in sediment transport models and highlights the transformative role of artificial intelligence (AI), specifically machine learning, in improving accuracy and computational efficiency. Extensive experiments were carried out in the Riba-Roja reservoir, which is located in the northeastern Iberian Peninsula. The accumulated sediment volume (ASV) curve was used to calibrate these experiments. The optimal ASV curve was found to be very close to the experimental data, with only minor differences in upstream areas. The results revealed a consistent rate of sediment transport and settling. Furthermore, the study investigated the capabilities of deep neural networks (DNNs) in predicting ASV curves and observing variable performance. In essence, the study highlights AI's potential for enhancing sediment transport models.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshMachine learning
dc.subject.lcshArtificial intelligence
dc.subject.otherSediment transport
dc.subject.otherMonte Carlo method
dc.subject.otherArtificial intelligence
dc.subject.otherMachine learning
dc.subject.otherHydraulic modeling
dc.titleOptimizing sediment transport models by using the Monte Carlo simulation and deep neural network (DNN): A case study of the Riba-Roja reservoir
dc.typeArticle
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacIntel·ligència artificial
dc.contributor.groupUniversitat Politècnica de Catalunya. Geo2Aqua - Monitoring, modelling and geomatics for hydro-geomorphological processes
dc.identifier.doi10.1016/j.envsoft.2024.105979
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1364815224000409
dc.rights.accessOpen Access
local.identifier.drac38076976
dc.description.versionPostprint (published version)
local.citation.authorDehghan-Souraki, D.; López-Gómez, D.; Blade, E.; Larese, A.; Sanz-Ramos, M.
local.citation.publicationNameEnvironmental modelling & software
local.citation.numberArticle 105979


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