The narxnn time series prediction model for ground subsidence caused by construction of metro station

How to precisely predict the ground subsidence induced by metro station construction becomes the key problem in urban underground engineering. A NARXNN time series prediction model is proposed due to the single linearity of traditional time series prediction model and its static limitation caused by ignorance of construction factor. In order to take the process of metro station construction into consideration nonlinearly and dynamically, construction impact factors, as a part of external inputs, are applied in this model that itself has delay unit and feedback architecture. Based on the NARXNN time series prediction model, the prediction results of ground subsidence induced by the Beihaibei station of Beijing metro line 6 construction show that: (1) Compared with traditional ARMA time series prediction model, NARXNN time series prediction model has a better adaptability and precision. (2) The NARXNN time series prediction model has a precise trend forecast at breakpoints of the settlement-time curve. (3) Multiple construction impact factors or subdividing each group of construction impact factors can be used to improve forecasting precision in using NARXNN time series prediction model. ©, 2015, Academia Sinica. All right reserved.