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An empirical comparison of machine learning techniques for dam behaviour modelling
Salazar González, Fernando; Toledo Municio, Miguel Ángel; Oñate Ibáñez de Navarra, Eugenio; Morán Moya, Rafael
Universitat Politècnica de Catalunya. Departament de Resistència dels Materials i Estructures en Enginyeria; Universitat Politècnica de Catalunya. GMNE - Grup de Mètodes Numèrics en Enginyeria
Predictive models are essential in dam safety assessment. Both deterministic and statistical models applied in the day-to-day practice have demonstrated to be useful, although they show relevant limitations at the same time. On another note, powerful learning algorithms have been developed in the field of machine learning (ML), which have been applied to solve practical problems. The work aims at testing the prediction capability of some state-of-the-art algorithms to model dam behaviour, in terms of displacements and leakage. Models based on random forests (RF), boosted regression trees (BRT), neural networks (NN), support vector machines (SVM) and multivariate adaptive regression splines (MARS) are fitted to predict 14 target variables. Prediction accuracy is compared with the conventional statistical model, which shows poorer performance on average. BRT models stand out as the most accurate overall, followed by NN and RF. It was also verified that the model fit can be improved by removing the records of the first years of dam functioning from the training set.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i preses
Dam safety
Neural networks (Computer science)
Machine learning
Dam monitoring
Dam safety
Machine learning
Boosted regression trees
Neural networks
Random forests
Support vector machines
Leakage flow
Preses (Enginyeria) -- Mesures de seguretat
Aprenentatge automàtic

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