Spatio-Temporal Sequence Prediction of Diversion Tunnel Based on Machine Learning Multivariate Data Fusion

The safety monitoring of hydraulic structures is an important measure to ensure the safe construction and operation of water diversion projects. The traditional data analysis and prediction of water conservancy monitoring mostly uses geometric models, and the accuracy of short-term prediction is reasonable, while the accuracy of long-term prediction is greatly reduced. Moreover, the traditional time series analysis method only considers the temporal correlation of the monitoring time series, but does not consider the spatial correlation between the multivariate monitoring time series, and can not make full use of the spatio-temporal correlation information between the multivariate monitoring data. To solve the above problems, this paper proposes a spatio-temporal prediction method, ARIMA-b-DLSSVM, which integrates multiple time series. The model is based on least square support vector machine (LSSVM) for multivariate data fusion, auto-regressive integral moving average (ARIMA) model for trend extraction, bisquare spatial basis to establish spatial correlation of monitoring data, and discounted least square method (DLS) for model optimization. The results show that the accuracy of ARIMA-b-DLSSVM long-term prediction is higher than that of traditional model and single machine learning model. The spatio-temporal fusion of multivariate data can better predict the spatio-temporal sequence changes of diversion tunnels with large fluctuations.