Traditional GNSS vertical time series prediction and interpolation methods only consider time variables and have ob-vious limitations.This study takes into account the impact of geophysical effects and constructs a regression problem using temperature,atmospheric pressure,polar motion,and GNSS vertical time series data,uses the adaptive boost(AdaBoost)algorithm for modeling.To verify the prediction and interpolation performance of the model,the vertical time series from 4 GNSS stations were selected for analysis.The modeling experiment shows that compared to the Prophet model,the fitting accuracy of AdaBoost model has been improved by 35％.The prediction results indicate that within a 12 month prediction peri-od,the MAE values of the AdaBoost model at four GNSS stations are approximately 4.0～4.5 mm,and the RMSE values are approximately 5.0～6.0 mm.The interpolation experiment shows that compared to the cubic spline interpolation method,the accuracy of AdaBoost interpolation model has been improved by about 15％-28％.Our experiments have shown that the Ada-Boost model considering geophysical effects can be applied to the prediction and interpolation of GNSS vertical time series.
GNSS vertical time seriesgeophysical effectspredictioninterpolationadaptive boosting algorithm
NETL
NSTL
万方数据
