Time Series Prediction of Dam Deformation Using a Hybrid STL–CNN–GRU Model Based on Sparrow Search Algorithm Optimization

Abstract

During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service status of dams. Therefore, an accurate deformation prediction is an important part of dam safety monitoring. However, due to multiple factors, dam deformation data often tend to be highly volatile, and most existing deformation estimation techniques employ a single algorithm, which may not effectively capture the potential change process. A hybrid model for dam deformation prediction has been proposed to overcome this problem. First, dam deformation data are decomposed into three components by seasonal and trend decomposition using loess. Second, a convolutional neural network–gated recurrent unit (GRU) hybrid model, which optimizes hyperparameters using the sparrow search algorithm, is used to capture the nonlinear relationships that exist in each component. Finally, the final prediction result of dam deformation is the comprehensive output of multiple submodules. The deformation monitoring data (period: 2009–2019) of a parabolic variable-thickness double-curved arch dam located in China are considered as the survey target. The test results indicate that the proposed model is suitable for short-term and long-term prediction and outperforms other models in terms of higher robustness to abnormal sequences than other conventional models (R² differs by 5.50% and 7.87%, respectively, in short-term and long-term predictions for different measurement points, while other models differ by 9.78% to reach 15.71%, respectively). Among the models studied, the GRU shows better robustness to abnormal series than the LSTM with good prediction accuracy, fewer parameters, and a simpler structure. Hence, the GRU can be employed for dam deformation prediction in practical engineering.

Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model

“…For example, mathematical statistics, structural analysis and artificial intelligence algorithms have been utilized in the studies of variation law, early warning and risk analysis related to the deformation of dams for decades [18][19][20]. Recently, with the rapid development of artificial intelligence algorithms, artificial neural networks [21][22][23], grey system models [24][25][26], clustering algorithms [27][28][29] and intelligent optimization algorithms [30][31][32] have been widely applied in the deformation prediction of hydraulic structure engineering. These algorithms are able to overcome the shortcomings of traditional prediction models in terms of multidimensional input, model adaptive learning and overfitting.…”

A Combined Method for Short-Term Load Forecasting Considering the Characteristics of Components of Seasonal and Trend Decomposition Using Local Regression

“…(1 STL uses locally estimated scatterplot smoothing (Loess) to extract smooth estimates for three components; it consists of two recursive processes, the inner loop and the outer loop, and has good robustness. The inner loop, based on Loess, smooths the seasonal and trend components, while the outer loop computes robust weights based on the residual component to reduce the influence of time series outliers on the residuals [16]. The calculation formula for robust weights is shown in formulas (2) and (3).…”

Development of a Time Series E-Commerce Sales Prediction Method for Short-Shelf-Life Products Using GRU-LightGBM

“…The advantage of the STL method is its robust adaptation to outliers in the data. It can be successfully applied in handling the case of a large number of time series data with great volatility and instability [39].…”