Prediction of Water Conservancy Facilities Deformation Integrating SARIMA and BiLSTM

Prediction of water conservancy facilities deformation can effectively judge their operation state. Water conservancy facilities safety monitoring data is time series data, which has both tendency and seasonality. In order to obtain more accurate prediction results, a prediction model based on seasonal autoregressive differential moving average (SARIMA) model and bidirectional long and short time memory (BiLSTM) network is proposed in this paper, this model solves the problem that the correlation between forward and backward in data cannot be fully utilized for prediction. In this model, SARIMA model is used to predict the linear components of deformation data, BiLSTM model is used to predict the nonlinear components of deformation data. The model can better extract the nonlinear relationships in historical data and improve the prediction accuracy. SARIMA-BiLSTM model is established based on the monitoring data of 4# diversion culvert of a hydropower station, then the model is used to predict the time series of crack meter opening and closing gap. The prediction result of this model is compared with results of back propagation (BP) neural network model, SARIMA model and SARIMA-LSTM model. The comparison results prove that the prediction accuracy is effectively improved by the proposed model.