Browse > Article
http://dx.doi.org/10.7848/ksgpc.2021.39.1.23

Estimation of GNSS Zenith Tropospheric Wet Delay Using Deep Learning  

Lim, Soo-Hyeon (Dept. of Geoinformation Engineering, Sejong University)
Bae, Tae-Suk (Dept. of Geoinformation Engineering, Sejong University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.39, no.1, 2021 , pp. 23-28 More about this Journal
Abstract
Data analysis research using deep learning has recently been studied in various field. In this paper, we conduct a GNSS (Global Navigation Satellite System)-based meteorological study applying deep learning by estimating the ZWD (Zenith tropospheric Wet Delay) through MLP (Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) models. Deep learning models were trained with meteorological data and ZWD which is estimated using zenith tropospheric total delay and dry delay. We apply meteorological data not used for learning to the learned model to estimate ZWD with centimeter-level RMSE (Root Mean Square Error) in both models. It is necessary to analyze the GNSS data from coastal areas together and increase time resolution in order to estimate ZWD in various situations.
Keywords
Deep Learning; Global Navigation Satellite System; Zenith Tropospheric Wet Delay; Meteorological Data;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Bae, T.-S. (2018), Accuracy analysis of GNSS-based public surveying and proposal for work processes, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 6, pp. 457-467.   DOI
2 Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R., and Ware, R.H. (1992), GPS meteorology-remote sensing of atmospheric water vapor using the Global Positioning System, Journal of Geophysical Research, Vol. 97, No. D14, pp. 15787-15801.   DOI
3 GDC (2020), Global Navigation Satellite System Integrated Data Center home page, http://gnssdata.or.kr (last date accessed: 20 November 2020).
4 Hofmann-Wellenhof, B., Lichtenegger, H., and Collines, J. (2001), GPS Theory and Practice, Springer-Verlag Wien New York, Wien, Austria, 382p.
5 Jason, H. (2020), Using machine learning to Nowcast precipitation in high resolution, Google AI Blog, https://ai.googleblog.com/2020/01/using-machine-learning-tonowcast.html (last date accessed: 10 November 2020).
6 Katsougiannopoulos, S. and Pikridas, C. (2009), Prediction of zenith tropospheric delay by multi-layer perceptron, Journal of Applied Geodesy, Vol. 3, No. 4, pp. 223-229.   DOI
7 Kim, S.-K. and Bae, T.-S. (2012), Long-term analysis of tropospheric delay and ambiguity resolution rate of GPS data, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 30, No. 6-2, pp. 673-680.   DOI
8 Kim, J.S. and Bae, T.-S. (2015), Comparative analysis of GNSS precipitable water vapor and meteorological factors, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 4, pp. 317-324.   DOI
9 Kim, H.-U. and Bae, T.-S. (2017), Preliminary study of deep learning-based precipitation prediction, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 5, pp. 423-429.   DOI
10 Kim, S.-K. and Bae, T.-S. (2018), Long-term GNSS analysis for local geodetic datum after 2011 Tohoku earthquake, The Journal of Navigation, Vol. 71, No. 1, pp. 117-133.   DOI
11 KMA (2020), Korea Meteorological Administration home page, http://www.kma.go.kr/home/index.jsp (last date accessed: 10 November 2020).
12 Nam, J.Y. and Song, D.S. (2019), Retrieval biases analysis on estimation of GNSS precipitable water vapor by tropospheric zenith hydrostatic models, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 37, No. 4, pp. 233-242.   DOI
13 Olah, C. (2015), Understanding LSTM Networks, Colah's Blog, http://colah.github.io/posts/2015-08-UnderstandingLSTMs (last date accessed: 23 December 2020).
14 Saastamoinen, J. (1973), Contribution to theory of atmospheric refraction, Bulletin Geodesique, Vol. 107, pp. 13-34.   DOI
15 Selbesoglu, M.O. (2020), Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data, Engineering Science and Technology, an International Journal, Vol. 23, No. 5, pp. 967-972.   DOI
16 Seo, Y.-M. (2019), Analysis of Prediction Accuracy of Fine Dust Concentration for Seoul Region Using LSTM Model, Master's thesis, Sejong University, Seoul, Korea, 77p.