A Deformation Prediction Model of High Arch Dams in the Initial Operation Period Based on PSR-SVM-IGWO

Abstract

The deformation prediction of the dam in the initial stage of operation is very important for the safety of high dams. A hybrid model integrating chaos theory, support vector machine (SVM), and an improved Grey Wolf Optimization (IGWO) algorithm is developed for deformation prediction of dam in the initial operation period. Firstly, the chaotic characteristics of the dam deformation time series will be identified, mainly using the Lyapunov exponent method, the correlation dimension method, and the Kolmogorov entropy method. Secondly, the SVM-IGWO model based on phase space reconstruction (PSR) is established for deformation forecasting of the dam in the initial operation period. Taking SVM as the core, the deformation time series is reconstructed in phase space to determine the input variables of SVM and the GWO algorithm is improved to realize the optimization of SVM parameters. Finally, take the actual monitoring displacement of Xiluodu super-high arch dam as an example. The engineering application example shows that, compared with the existing models, the prediction accuracy of the PSR-SVM-IGWO model established in this paper is improved.

Identification of Trends in Dam Monitoring Data Series Based on Machine Learning and Individual Conditional Expectation Curves

“…The use of artificial intelligence (AI) models applied to dam safety has proliferated in recent times. Numerous scientific references related to the use of ML, DL, or hybrid models with other types of models such as statistical, time series, or physics-based numerical models can be found [6][7][8][9][10][11][12]. Machine Learning models have shown good performance in monitoring data prediction and are more accessible to interpretation than DL models.…”

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“…The precision of these ML models has increased in comparison to multiple linear regression 25,26 -based, partial least squares regression 27,28 -based, and stepwise regression 29,30 -based HST and HTT models, especially for concrete dam displacement prediction with small sample and short-term prediction periods. 18,[31][32][33][34] Nevertheless, these ML models still have issues, one of which is that the essence of these models still relies on the statistical models that use hydrostatic pressure, temperature, and aging components as inputs for training, and the reasonable selection of explanatory variables is crucial to the accuracy; another reason is that the majority of these algorithms only support multiinput single-output patterns, 22 which reflect the local displacement characteristics of a unitary measurement point rather than the overall response to the dam displacement. Once the scale of the dam displacement measurement points is enormous, it is time-consuming and inefficient to model each point individually.…”

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