A Time-varying Kalman Model for Dam Monitoring Data Prediction

Based on time-varying requirements of prediction model for dam monitoring data,the forgetting factor is introduced to set up a forgotten matrix to give prominence to the contributions of recent data.Then the IWRLS algorithm is made to achieve updating model parameters at real-time.On this basis,in order to reflect the physical meaning and complete the filtering operation at the same time,a statistical model and ARMA are introduced into the Kalman filter equations.In the equations,state equation is established by self-variable which reflects the state characteristics with ARMA,and observation equation is established by dependent variable which reflects physical meaning with statistical models.So considering the white noise,the time-varying Kalman prediction model is established with the comprehensive functions.Case analysis shows that the fitting and forecast accuracy of time-varying Kalman model are superior to those traditional statistical models.