A Solution for Ground Subsidence Prediction of Time Series Based on Autoregression EIV Model with Inequality Constraints

Ground subsidence monitoring is an effective method to forecast geological hazard, and time series model is the main model for ground subsidence prediction. To take into account both the observation errors existing in coefficient matrix and observation vector, in this contribution, the time series model of ground subsidence is developed to improve errors-in-variables (EIV) model, while the traditional model only takes into account the observation error existing in observation error. Besides, to improve efficiency and accuracy of computation model parameters, prior information is utilized to establish EIV model with inequality constraints, and the inequality constraints model for ground subsidence prediction is converted into quadratic programming of nonlinear model. And the iterative algorithm which is combined with median function is proposed. The efficiency and feasibility of the presented algorithm are verified through the instances, which are compared with the traditional least squares estimation algorithm and current algorithm for EIV model.