![](/images/graphics-bg.png)
Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm
Joint Authors
Zhang, Xuan
Chen, Xudong
Li, Junjie
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-13
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Statistical model is a traditional safety diagnostic model for dam seepage.
It can hardly display the nonlinear relationship between dam seepage and the load sets and has the disadvantage of poor extension prediction.
In this paper, the theories of Back Propagation Neural Network (BPNN) combined with Genetic Algorithm (GA) are applied to the seepage prediction model.
Taking a typical dam in China as an example, the prediction results of BPNN-GA model and statistical model are compared with the monitoring values.
The results show that the improved dam seepage model enhances the ability of nonlinear mapping and generalization and makes the seepage prediction more accurate and reasonable in the near future.
According to the established criterion, the safety state of the dam in flood season is evaluated.
American Psychological Association (APA)
Zhang, Xuan& Chen, Xudong& Li, Junjie. 2020. Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1193211
Modern Language Association (MLA)
Zhang, Xuan…[et al.]. Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm. Mathematical Problems in Engineering No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1193211
American Medical Association (AMA)
Zhang, Xuan& Chen, Xudong& Li, Junjie. Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1193211
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193211