Correction of environmental loading effects in GNSS vertical coordinate time series based on generalized regression neural network
Environmental loading typically contributes to the non-linear change of GNSS vertical coordinate time series,and addressing its influence is a crucial component of GNSS coordinate time series research.The model construction and parameter solution processes used in traditional method of environmental loading correction based on physical model need to involve various simplifications and approximations,resulting in the correction is not precise enough.In this paper,generalized regression neural network(GRNN),a data-driven method,is introduced to improve the effect of environmental loading correction.Taking the GNSS vertical coordinate time series of the Sichuan-Yunnan regional stations as the research object,we first separate the coordinate time series based on the Variational Bayesian Independent Component Analysis(vbICA)technique,analyze the obtained periodic components,and find that the atmospheric and land water storage loadings are the important causes of the seasonal changes of the station coordinates.The influence of two environmental loadings in the coordinate time series is then eliminated by using GRNN to build a connection between the data of environmental parameters linked to the atmosphere and land water storage and the coordinate time series data.After correcting the effects of atmospheric and land water storage loadings by GRNN modeling,the RMS values of the stations coordinate residual series are reduced by 21.56％on average,while the average reduction is only 9.29％after correcting by the traditional physical model method.It can be considered that the data-driven approach based on GRNN is more effective.Additionally,the GRNN models are established taking into account the effects of four climatic factors:temperature(belowground),ice concentration,specific humidity,and rainfall rate.The results show that the temperature(below ground)factor has a slightly greater impact on the vertical coordinates of the stations in the Sichuan-Yunnan region than the other three.
GNSS coordinate time seriesEnvironmental loadingGeneralized regression neural networkData-driven
万方数据
维普
