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Article

A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure

1
International Centre for Numerical Methods in Engineering (CIMNE), 08034 Barcelona, Spain
2
Flumen Institute, Universitat Politècnica de Catalunya (UPC BarcelonaTech)—International Centre for Numerical Methods in Engineering (CIMNE), 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Roohollah Noori
Water 2022, 14(15), 2416; https://doi.org/10.3390/w14152416
Received: 27 June 2022 / Revised: 21 July 2022 / Accepted: 27 July 2022 / Published: 4 August 2022
(This article belongs to the Section Water Resources Management, Policy and Governance)
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. View Full-Text
Keywords: machine learning; Iber; off-stream reservoirs; dam breach; floods; random forest; surrogate model machine learning; Iber; off-stream reservoirs; dam breach; floods; random forest; surrogate model
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MDPI and ACS Style

Silva-Cancino, N.; Salazar, F.; Sanz-Ramos, M.; Bladé, E. A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure. Water 2022, 14, 2416. https://doi.org/10.3390/w14152416

AMA Style

Silva-Cancino N, Salazar F, Sanz-Ramos M, Bladé E. A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure. Water. 2022; 14(15):2416. https://doi.org/10.3390/w14152416

Chicago/Turabian Style

Silva-Cancino, Nathalia, Fernando Salazar, Marcos Sanz-Ramos, and Ernest Bladé. 2022. "A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure" Water 14, no. 15: 2416. https://doi.org/10.3390/w14152416

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