LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station

Abstract

The Danjiangkou hydropower station is a water source project for the middle line of the South-to-North Water Transfer Project in China. The dam is composed of riverbed concrete dam and earth rock dam on both banks, with a total length of 3442 m. Once the dam is wrecked, it will yield disastrous consequences. Therefore, it is very important to evaluate the dam safety behavior in time. Based on the long-term and short-term memory (LSTM) network, the deformation prediction models of the embankment dam of the Danjiangkou hydropower station are constructed. The models contain two LSTM layers, adopt the rectified linear unit function as the activation function and determine the super parameters of the models with Bayesian optimization algorithm. According to the settlement monitoring data of LD12ZT01 measuring point (dam crest 0 + 648) on the left bank of the embankment dam of the Danjiangkou hydropower station from July 2013 to March 2022, the LSTM and bidirectional LSTM models are constructed. In total, 80% of the monitoring data are taken as the training set data and 20% of the monitoring data are taken as the test set data. The mean absolute error, root mean square error and mean square error for the test set are 0.42978, 0.56456 and 0.31873 for partial least squares regression (PLSR), 0.35264, 0.47561 and 0.22621 for LSTM and 0.34418, 0.45400 and 0.20612 for bidirectional LSTM, respectively. The results show that the bidirectional LSTM model can obtain better deformation prediction value than the LSTM model and the PLSR. Then, the bidirectional LSTM model is used to predict the settlement value of LD16YT01 measuring point (dam crest 0 + 658) on the right bank, and the mean absolute error, root mean square error and mean square error for the test set are 0.5425, 0.66971 and 0.4520, respectively. This shows the bidirectional LSTM model can effectively predict the settlement value of the embankment dam of the Danjiangkou hydropower station.

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

“…Compared to the findings in reference [12] that employed LSTM for predicting tunnel surrounding rock deformations, our employment of LSTM for forecasting subway track foundation deformations yields similar predictive performance, achieving an accuracy of approximately 2 mm (RMSE). Similarly, when juxtaposed with the results from reference [13] utilizing the BiLSTM for predicting hydroelectric dam deformations, our employment of BiLSTM demonstrates comparable predictive performance, achieving an RMSE ranging from 0.4 to 0.5 mm. However, by synergizing the strengths of ensemble learning algorithms, our BiLSTM-AdaBoost model emerges as the superior choice for predicting subway settlement deformations.…”

“…For the prediction of the tunnel surrounding rock deformation, He and Chen [12] constructed an LSTM prediction network, achieving an average error of 2.16 mm. In the case of embankment dam deformation prediction, Wang et al [13] utilized a bidirectional LSTM (BiLSTM) network, demonstrating superior prediction performance compared to LSTM. The reason is that BiLSTM can simultaneously consider the preceding and succeeding information of sequential data, acquiring more comprehensive contextual information through forward and backward learning [14].…”

Concrete Dam Deformation Prediction Model Research Based on SSA–LSTM

“…With the increasing number of power stations and reservoirs in operation, the safe operation of reservoir dams is receiving heightened attention. The safe operation of reservoir dams is not only important for engineering safety but also directly impacts the safety of people's lives and property [1][2][3]. Therefore, for reservoir dams, health diagnosis is a systematic and routine task.…”

Deformable River Beds: A Numerical Study

“…Simulations of the hydrodynamic and dynamic changes in the relief of the riverbed were carried out, and the models for accurate calculation of changes in the layer level and the area of deposition and erosion were proposed [44][45][46][47]. In the work [48][49][50], a hydraulic experiment was conducted to study the hydraulic phenomena of the dam, comparing hydraulic surges and flow characteristics. It was found that although the sluice gates generated hydraulic surges similar to those in stationary dams, their supercritical flow increased downstream, which ultimately lengthened the overall hydraulic surge.…”