A New Global ZTD Forecast Model Based on Improved LSTM Neural Network

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 9606 - 9614
• Main Authors: He, Lin, Yao, Yibin, Xu, Chaoqian, Huan, Zhang, Tang, Feifei, Ji, Changquan, Liu, Zhuoya, Wu, Wentan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptation models; Algorithms; Atmospheric modeling; Data models; Delay; Delays; Forecasting models; Global navigation satellite system; Global navigation satellite system (GNSS); Kinematics; Long short-term memory; long short-term memory (LSTM); Mathematical models; Navigation; Navigation satellites; Navigation systems; Navigational satellites; Neural networks; Numerical models; Prediction models; Predictive models; Root-mean-square errors; Satellites; Troposphere; Zenith; zenith tropospheric delay
• DOI: 10.1109/JSTARS.2024.3391821
• ISSN: 1939-1404

Zenith tropospheric delay (ZTD), consisting of zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD), is a significant contributor to errors in precise positioning using the global navigation satellite system (GNSS) precise point positioning (PPP) and real-time kinematic techniques. Accurate and timely predictions of ZTD on a global scale are crucial for enhancing GNSS positioning accuracy and expediting convergence. This study proposes an innovative global tropospheric prediction model that leverages long short-term memory (LSTM) neural networks, aiming to achieve both high precision and long-term prediction capability for ZTD. The experimental data utilized were sourced from the Vienna Mapping Functions 3-Optimized zenith total delay (ZTD) dataset. This study delves further into the analysis of ZTD residuals by extracting periodic signals. The ZTD residuals were then utilized to train a modified LSTM neural network model, enabling the prediction of global residuals. The final ZTD predictions were obtained by combining the modified LSTM ZTD residual forecast component with the ZTD periodic component. Our results demonstrate that the average root-mean-square error (RMSE) of the modified LSTM-ZTD model in 2020 was 1.44 cm. In addition, the average RMSE of the forecasted ZTD during spring, summer, autumn, and winter was found to be 1.43 cm, 1.47 cm, 1.56 cm, and 1.36 cm, respectively. Through the integration of the LSTM neural network and the ZTD periodic signal extracted using a physical algorithm, this work has successfully enhanced the accuracy and time span of ZTD forecasts on a global scale.