Transformer-Based Global Zenith Tropospheric Delay Forecasting Model

نویسندگان

چکیده

Zenith tropospheric delay (ZTD) plays an important role in high-precision global navigation satellite system (GNSS) positioning and meteorology. At present, commonly used ZTD forecasting models comprise empirical, meteorological parameter, neural network models. The empirical model can only fit approximate periodic variations, its accuracy is relatively low. of the parameter depends heavily on parameters. recurrent (RNN) suitable for short-term series data prediction, but long-term series, prediction clearly reduced. Long memory (LSTM) has superior series; however, LSTM complex, cannot be parallelized, time-consuming. In this study, we propose a novel time-series utilizing transformer-based machine-learning methods that are popular natural language processing (NLP) ZTD, training parameters provided by geodetic observing (GGOS). proposed transformer leverages self-attention mechanisms encoder decoder modules to learn complex patterns dynamics from long time series. numeric results showed root mean square error (RMSE) were 1.8 cm bias, STD, MAE, R 0.0, 1.7, 1.3, 0.95, respectively, which LSTM, RNN, convolutional (CNN), GPT3 We investigated distribution these indicators, demonstrated continents was maritime space at high latitudes low latitude. addition overall improvement, forecast also mitigates variations time, thereby guaranteeing globally. This study provides method estimate could potentially contribute precise GNSS

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs14143335