2026-04-17T07:49:50Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3717762026-02-07T07:41:39Zcom_2072_1033col_2072_452950

A machine learning-based surrogate model for the identification of risk zones due to off-stream reservoir failure Silva Cancino, Nathalia Salazar González, Fernando Sanz Ramos, Marcos Bladé i Castellet, Ernest Universitat Politècnica de Catalunya. Doctorat en Enginyeria Civil Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental Centre Internacional de Mètodes Numèrics en Enginyeria Universitat Politècnica de Catalunya. FLUMEN - Dinàmica Fluvial i Enginyeria Hidrològica Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i preses Dams -- Risk assessment Machine learning Iber Off-stream reservoirs Dam breach Floods Random forest Surrogate model Preses -- Avaluació del risc Approximately 70,000 Spanish off-stream reservoirs, many of them irrigation ponds, need to be evaluated in terms of their potential hazard to comply with the new national Regulation of the Hydraulic Public Domain. This requires a great engineering effort to evaluate different scenarios with two-dimensional hydraulic models, for which many owners lack the necessary resources. This work presents a simplified methodology based on machine learning to identify risk zones at any point in the vicinity of an off-stream reservoir without the need to elaborate and run full two-dimensional hydraulic models. A predictive model based on random forest was created from datasets including the results of synthetic cases computed with an automatic tool based on the two-dimensional numerical software Iber. Once fitted, the model provided an estimate on the potential hazard considering the physical characteristics of the structure, the surrounding terrain and the vulnerable locations. Two approaches were compared for balancing the dataset: the synthetic minority oversampling and the random undersampling. Results from the random forest model adjusted with the random undersampling technique showed to be useful for the estimation of risk zones. On a real application test the simplified method achieved 91% accuracy. This work was partially funded by the Spanish Ministry of Science, Innovation and Universities through the Projects ACROPOLIS (RTC2019-007343-5), TRISTAN (RTI2018-094785-B-I00) and DOLMEN (PID2021-122661OB-I00), as well as by the Spanish Ministry of Economy and Competitiveness, through the “Severo Ochoa Programme for Centres of Excellence in R & D” (CEX2018-000797-S), and by the Generalitat de Catalunya through the CERCA Program. Peer Reviewed Postprint (published version) 2022-08 Article Silva, N. [et al.]. A machine learning-based surrogate model for the identification of risk zones due to off-stream reservoir failure. "Water (Switzerland)", Agost 2022, vol. 14, núm. 15, p. 2416:1-2416:25. 2073-4441 https://hdl.handle.net/2117/371776 10.3390/w14152416 eng https://www.mdpi.com/2073-4441/14/15/2416/htm info:eu-repo/grantAgreement/RTC2019-007343-5 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094785-B-I00/ES/NUEVAS HERRAMIENTAS COMPUTACIONALES PARA EL ESTUDIO DE SEGURIDAD DE PRESAS BASADO EN ANALISIS DE FIABILIDAD/ info:eu-repo/grantAgreement/PID2021-122661OB-I00 http://creativecommons.org/licenses/by/4.0/ Open Access Attribution 4.0 International application/pdf