An Improved Long Short-Term Memory Model for Dam Displacement Prediction

Jun Zhang, Xiyao Cao, Jiemin Xie and Pangao Kou

Mathematical Problems in Engineering, 2019, vol. 2019, 1-14

Abstract:

Displacement plays a vital role in dam safety monitoring data, which adequately responds to security risks such as the flood water pressure, extreme temperature, structure deterioration, and bottom bedrock damage. To make accurate predictions, former researchers established various models. However, these models’ input variables cannot efficiently reflect the delays between the external environment and displacement. Therefore, a long short-term memory (LSTM) model is proposed to make full use of the historical data to reflect the delays. Furthermore, the LSTM model is improved to optimize the performance by making variables more physically reasonable. Finally, a real-world radial displacement dataset is used to compare the performance of LSTM models, multiple linear regression (MLR), multilayer perceptron (MLP) neural networks, support vector machine (SVM), and boosted regression tree (BRT). The results indicate that the LSTM models can efficiently reflect the delays and make the variables selection more convenient and the improved LSTM model achieves the best performance by optimizing the input form and network structure based on a clearer physical meaning.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2019/6792189.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2019/6792189.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6792189

DOI: 10.1155/2019/6792189

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().