Dam slope deformation prediction method based on BP autoregressive algorithm

Qingyuan Luo; Lina Liu; Yuge Wang

doi:10.1117/12.3025505

8 April 2024 Dam slope deformation prediction method based on BP autoregressive algorithm

Qingyuan Luo, Lina Liu, Yuge Wang

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130902N (2024) https://doi.org/10.1117/12.3025505
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

As the prediction structure of dam slope deformation is mostly unidirectional and the prediction target is not clear, which is easy to lead to excessive prediction error. BP autoregressive algorithm was used to design the dam slope deformation prediction method. Firstly, the prediction data set was established based on the actual prediction requirements and standards. Then, the specific prediction targets were set using the multi-level prediction form, and the multi-objective three-dimensional prediction structure was established. Finally, the deformation prediction model was built through the BP auto-regression algorithm, and the more accurate prediction was achieved by using the dynamic regression correction. The experimental results show that the proposed dam slope deformation prediction method based on BP autoregressive algorithm has higher prediction accuracy and stability, compared with the traditional BP neural network, Beidou GNSS and multi-cluster echo slope deformation prediction methods, the RMSE, MAPE, and MAE of predicted values are only 0.35mm, 5.78%, and 0.32mm, respectively, which can provide high-precision data for dam safety monitoring.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingyuan Luo, Lina Liu, and Yuge Wang "Dam slope deformation prediction method based on BP autoregressive algorithm", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130902N (8 April 2024); https://doi.org/10.1117/12.3025505

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available