Coupling prediction model for long-term displacements of arch dams based on long short-term memory network

The long-term safety and health monitoring of large dams has attracted increasing attention. In this paper, coupling prediction models based on long short-term memory (LSTM) network are proposed for the long-term deformation of arch dams. Principal component analysis (PCA) and moving average (MA) method, adopted to make dimension reduction for the input variables, are respectively combined with the LSTM to achieve two coupling prediction models, that is, LSTM-PCA and LSTM-MA. Lijiaxia arch dam, which has been in operation over 20 years, is taken as an analysis example. Compared with the traditional hydrostatic-seasonal-time model, the hydrostatic-seasonal-time thermal model, and the multilayer perceptron model, the proposed models show more effectiveness concerning the predicted displacements of the arch dam. The accuracy of the predicted results from the coupling prediction models is better. Furthermore, the coupling prediction models could capture the long-term characteristics and provide better prediction with short monitoring data. Compared with the LSTM-PCA model, the LSTM-MA model is more suitable for engineering applications due to its convenience.