Proceedings of the 2016 International Conference on Automatic Control and Information Engineering

Prediction of Arch Dam Temperature Based on ARIMA-RTA Model

Authors
Guozheng Sun, Zheyu Zhang, Peng Qin
Corresponding Author
Guozheng Sun
Available Online October 2016.
DOI
10.2991/icacie-16.2016.22How to use a DOI?
Keywords
arch dam, temperature monitoring, prediction, ARIMA, RTA
Abstract

The analysis and prediction of arch dam temperature is very meaningful to engineering maintenance and disaster prevention. Taking the arch dam temperature monitoring data as the research object, the ARIMA-RTA combination forecasting model is improved by using the ARIMA model's higher fitting capacity and the RTA isoperimetric recursive prediction to improve the prediction length of arch dam monitoring data. As a test, the prediction model is applied to the project of "bailianya", and the result shows that the model has carefully-defined physical conception and thus brings about vast range of prospect for application due to its high precision and noise immunity.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Automatic Control and Information Engineering
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
10.2991/icacie-16.2016.22
ISSN
2352-5401
DOI
10.2991/icacie-16.2016.22How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Guozheng Sun
AU  - Zheyu Zhang
AU  - Peng Qin
PY  - 2016/10
DA  - 2016/10
TI  - Prediction of Arch Dam Temperature Based on ARIMA-RTA Model
BT  - Proceedings of the 2016 International Conference on Automatic Control and Information Engineering
PB  - Atlantis Press
SP  - 92
EP  - 95
SN  - 2352-5401
UR  - https://doi.org/10.2991/icacie-16.2016.22
DO  - 10.2991/icacie-16.2016.22
ID  - Sun2016/10
ER  -