dc.creator | Bui, Kien-Trinh T. | es |
dc.creator | Torres, José F. | es |
dc.creator | Gutiérrez Avilés, David | es |
dc.creator | Nhu, Viet-Ha | es |
dc.creator | Bui, Dieu Tien | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.date.accessioned | 2022-04-06T10:58:18Z | |
dc.date.available | 2022-04-06T10:58:18Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Bui, K.T., Torres, J.F., Gutiérrez Avilés, D., Nhu, V., Bui, D.T. y Martínez Álvarez, F. (2022). Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm. Computer-Aided Civil and Infrastructure Engineering, January 2022, 1-19. | |
dc.identifier.issn | 1467-8667 | es |
dc.identifier.uri | https://hdl.handle.net/11441/131824 | |
dc.description.abstract | The safety operation and management of hydropower dam play a critical role
in social-economic development and ensure people’s safety in many countries;
therefore, modeling and forecasting the hydropower dam’s deformations with
high accuracy is crucial. This research aims to propose and validate a new model
based on deep learning long short-term memory (LSTM) and the coronavirus
optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the defor mations of the hydropower dam. The second-largest hydropower dam of Viet nam, located in the Hoa Binh province, is focused. Herein, we used the LSTM
to establish the deformation model, whereas the CVOA was utilized to opti mize the three parameters of the LSTM, the number of hidden layers, the learn ing rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is
assessed by comparing its forecasting performance with state-of-the-art bench marks, sequential minimal optimization for support vector regression, Gaussian
process, M5’ model tree, multilayer perceptron neural network, reduced error
pruning tree, random tree, random forest, and radial basis function neural net work. The result shows that the proposed CVOA-LSTM model has high fore casting capability (R2 = 0.874, root mean square error = 0.34, mean absolute
error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM
is a new tool that can be considered to forecast the hydropower dam’s deforma tions. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C21 | es |
dc.format | application/pdf | es |
dc.format.extent | 19 | es |
dc.language.iso | eng | es |
dc.publisher | Wiley | es |
dc.relation.ispartof | Computer-Aided Civil and Infrastructure Engineering, January 2022, 1-19. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | PID2020-117954RB-C21 | es |
dc.relation.publisherversion | https://onlinelibrary.wiley.com/doi/10.1111/mice.12810 | es |
dc.identifier.doi | 10.1111/mice.12810 | es |
dc.journaltitle | Computer-Aided Civil and Infrastructure Engineering | es |
dc.publication.issue | January 2022 | es |
dc.publication.initialPage | 1 | es |
dc.publication.endPage | 19 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |