TY - JOUR
T1 - DRLSTM
T2 - A dual-stage deep learning approach driven by raw monitoring data for dam displacement prediction
AU - Li, Mingchao
AU - Li, Minghao
AU - Ren, Qiubing
AU - Li, Heng
AU - Song, Lingguang
N1 - Funding Information:
This research was jointly funded by the National Natural Science Foundation of China (Grant Nos. 51879185 and 52179139 ) and the Open Fund of Hubei Key Laboratory of Construction and Management in Hydropower Engineering (Grant No. 2020KSD06).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/1
Y1 - 2022/1
N2 - Dam displacement is an important indicator of the overall dam health status. Numerical prediction of such displacement based on real-world monitoring data is a common practice for dam safety assessment. However, the existing methods are mainly based on statistical models or shallow machine learning models. Although they can capture the timing of the dam displacement sequence, it is difficult to characterize the complex coupling relationship between displacement and multiple influencing factors (e.g., water level, temperature, and time). In addition, input factors of most dam displacement prediction models are artificially constructed based on modelers’ personal experience, which lead to a loss of valuable information, thus prediction power, provided by the full set of raw monitoring data. To address these problems, this paper proposes a novel dual-stage deep learning approach based on one-Dimensional Residual network and Long Short-Term Memory (LSTM) unit, referred to herein as the DRLSTM model. In the first stage, the raw monitoring sequence is processed and spliced with convolution to form a combined sequence. After the timing information is extracted, the convolution direction is switched to learn the complex relationship between displacement and its influencing factors. LSTM is used to extract this relationship to obtain Stage I prediction. The second stage takes the difference between the actual measurement and the Stage I prediction as inputs, and LSTM extracts the stochastic features of the monitoring system to obtain Stage II prediction. The sum of two stage predictions forms the final prediction. The DRLSTM model only requires raw monitoring data of water level and temperature to accurately predict displacement. Through a real-world comparative study against four commonly used shallow learning models and three deep learning models, the root mean square error and mean absolute error of our proposed method are the smallest, being 0.198 mm and 0.149 mm respectively, while the correlation coefficient is the largest at 0.962. It is concluded that the DRLSTM model performance well for evaluating dam health status.
AB - Dam displacement is an important indicator of the overall dam health status. Numerical prediction of such displacement based on real-world monitoring data is a common practice for dam safety assessment. However, the existing methods are mainly based on statistical models or shallow machine learning models. Although they can capture the timing of the dam displacement sequence, it is difficult to characterize the complex coupling relationship between displacement and multiple influencing factors (e.g., water level, temperature, and time). In addition, input factors of most dam displacement prediction models are artificially constructed based on modelers’ personal experience, which lead to a loss of valuable information, thus prediction power, provided by the full set of raw monitoring data. To address these problems, this paper proposes a novel dual-stage deep learning approach based on one-Dimensional Residual network and Long Short-Term Memory (LSTM) unit, referred to herein as the DRLSTM model. In the first stage, the raw monitoring sequence is processed and spliced with convolution to form a combined sequence. After the timing information is extracted, the convolution direction is switched to learn the complex relationship between displacement and its influencing factors. LSTM is used to extract this relationship to obtain Stage I prediction. The second stage takes the difference between the actual measurement and the Stage I prediction as inputs, and LSTM extracts the stochastic features of the monitoring system to obtain Stage II prediction. The sum of two stage predictions forms the final prediction. The DRLSTM model only requires raw monitoring data of water level and temperature to accurately predict displacement. Through a real-world comparative study against four commonly used shallow learning models and three deep learning models, the root mean square error and mean absolute error of our proposed method are the smallest, being 0.198 mm and 0.149 mm respectively, while the correlation coefficient is the largest at 0.962. It is concluded that the DRLSTM model performance well for evaluating dam health status.
KW - Dam displacement prediction
KW - Deep learning
KW - Long and short-term memory units
KW - Raw monitoring data
KW - Residual convolutional neural network
UR - http://www.scopus.com/inward/record.url?scp=85121964671&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2021.101510
DO - 10.1016/j.aei.2021.101510
M3 - Journal article
AN - SCOPUS:85121964671
SN - 1474-0346
VL - 51
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
M1 - 101510
ER -