Improving reservoir inflow prediction via rolling window and deep learning-based multi-model approach: case study from Ermenek Dam, Turkey

Feizi, Hajar; Apaydín, Halit; Sattari, Mohammad Taghi; Colak, Muslume Sevba; Sibtain, Muhammad

doi:10.1007/s00477-022-02185-3

Cited by 10 publications

(1 citation statement)

References 51 publications

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“…Li et al [31] used a deep-stacked bidirectional LSTM neural network with a self-attention mechanism to capture the temporal dependencies of the original sensor data, and the method can deal with various missing data scenarios in dam monitoring system. Feizi et al [32] proposed a hybrid deep learning inflow prediction-rolling window framework for inflow prediction.…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Yang²,

Chen³

et al. 2022

Water

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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.

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Section: Introductionmentioning

confidence: 99%