Methods of enhancing forecasting capacity of subgrade settlement

Abstract

Due to the complication and randomness of dynamic change during settlement, the reliability and predictability of mathematical models are always limited in the settlement forecasting. In order to enhance the reliability and predictability of the mathematical forecasting model, using the settlement monitoring data of a high-speed railway roadbed in the loess region to establish the non-synchronization of the three exponential smoothing model, cross-certification analytic method and after-verification analytic method were used to compare and validate the fitting accuracy and forecasting accuracy on different synchronization models for getting the best reliable forecasting model; then, parallel-modification analytic method was used to modify forecasting results for enhancing forecasting capacity. The results show that the cross-certification analytic method and after-verification analytic method can provide a guarantee for the best forecasting model, and parallel-modification analytic method can not only enhance forecasting accuracy and forecasting length, but improve the comprehensive forecasting capacity as well. At the same time, the three methods can be applied to other mathematical models, because of simple principle, easy operation and strong adaptability.