Abstract：Aiming at the low accuracy of GPT2w and UNB3m regional tropospheric zenith total delay (ZTD) model, we discuss the feasibility of regional ZTD modeling based on machine learning method. Taking ZTD data calculated by GAMIT software (ZTD_GAMIT) for 31 consecutive days in 2021 from 13 IGS stations in California as an example, we propose an improved ZTD model using ZTD values estimated by longitude, latitude, geodetic height, day of year, daily hours, GPT2w or UNB3m empirical ZTD model (ZTD_GPT or ZTD_UNB) as inputs and ZTD_GAMIT as outputs based on random forest (RF) and back propagation neural network (BPNN). The experimental results show that compared with the GPT2w and UNB3m models, the prediction accuracy of the two improved regional ZTD models based on machine learning methods is improved, and the system bias is effectively improved. The root mean square error (RMSE) of BPNN and RF improved models with ZTD_UNB as inputs are 15.14 mm and 19.48 mm, respectively. The RMSE of BPNN and RF improved models with ZTD_GPT as inputs are 15.32 mm and 20.74 mm, respectively. The prediction accuracy of BPNN model is generally better than that of RF model, and has higher reliability.
WEI Min,YU Xuexiang,YANG Xu et al. Accuracy Analysis of Regional ZTD Modeling Based on Random Forest and Back Propagation Neural Network Machine Learning Method[J]. jgg, 2023, 43(7): 755-760.
