Article
Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm
Author/s | Bui, Kien-Trinh T.
Torres, José F. Gutiérrez Avilés, David Nhu, Viet-Ha Bui, Dieu Tien Martínez Álvarez, Francisco |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2022 |
Deposit Date | 2022-04-06 |
Published in |
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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 ... 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. |
Funding agencies | Ministerio de Ciencia, Innovación y Universidades (MICINN). España |
Project ID. | PID2020-117954RB-C21 |
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. |
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