Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Preliminary Study of Deep Learning-based Precipitation

  • Kim, Hee-Un (Dept. of Geoinformation Engineering, Sejong University) ;
  • Bae, Tae-Suk (Dept. of Geoinformation Engineering, Sejong University)
  • Received : 2017.10.10
  • Accepted : 2017.10.16
  • Published : 2017.10.31
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Abstract

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

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References

  1. Baek, J.H., Lee, J.W., Choe, B.G., and Cho, J.H. (2007), Processing strategy for near real time GPS precipitable water vapor retrieval, The Korean Space Science Society, Vol. 24, No. 4, pp. 275-284. (in Korean with English abstract)
  2. Bengio, Y., Courville, A., and Vincent, P. (2013), Representation learning: A review and new perspectives, IEEE transactions on pattern analysis and machine intelligence, Vol. 35, No. 8, pp. 1798-1828. https://doi.org/10.1109/TPAMI.2013.50
  3. Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R.A., 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. 14, pp. 15787-15801. https://doi.org/10.1029/92JD01517
  4. Davis, J.L., Herring, T.A., Sharpiro, I.I., Rogers, A.E.E., and Elgered, G. (1985), Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length, Radio Science, Vol. 20, No. 6, pp. 1593-1607. https://doi.org/10.1029/RS020i006p01593
  5. Elgered, G., Davis, J.L., Herring, T.A., and Shapiro, I.I. (1991), Geodesy by radio interferometry: Water vapor radiometry for estimation of the wet delay, Journal of Geophysical Research, Vol. 96, No. B4, pp. 6541-6555. https://doi.org/10.1029/90JB00834
  6. Ha, J.H., Lee, Y.H., and Kim, Y.H. (2016), Forecasting the precipitation of the next day using deep learning, Journal of The Korean Institute of Intelligent Systems, Vol. 26, No. 2, pp. 93-98. (in Korean with English abstract) https://doi.org/10.5391/JKIIS.2016.26.2.093
  7. Hopfield, H.S. (1969), Two-quartic tropospheric refractivity profile for correcting satellite data, Journal of Geophysical Research, Vol. 74, No. 18, pp. 4487-4499. https://doi.org/10.1029/JC074i018p04487
  8. Kang, B.S. and Lee, B.K. (2008), Predicting probability of precipitation using artificial neural network and mesoscale numerical weather prediction, Journal of The Korean Society of Civil Engineers, Vol. 28, No. 5B, pp. 485-493. (in Korean with English abstract)
  9. Kim, J.S. (2016), Enhancement of GNSS Precipitable Water Vapor Estimation and its Application to Weather Prediction, Master's thesis, Sejong University, Seoul, Korea, 144p.
  10. Kuligowski, R.J. and Barros, A.P. (1998), Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks, Wea. Forecasting, Vol. 13, No. 4, pp. 1194-1204. https://doi.org/10.1175/1520-0434(1998)013<1194:LPFFAN>2.0.CO;2
  11. Lee, S.H. and Lee, J.H. (2016), Customer churn prediction using RNN, Proceedings of the Korean Society of Computer Information Conference, Vol. 24, No. 2 pp. 45-48.
  12. Olah, C. (2015), Understanding LSTM networks, Colah's Blog, http://colah.github.io/posts/2015-08-Understanding-LSTMs/ (last date accessed: 27 August 2017).
  13. Saastamoinen, J. (1972), Atmospheric correction for troposphere and stratosphere in radio ranging of satellites, The Use of Artificial Satellites for Geodesy, pp. 247-252.
  14. Sachan, A. (2014), Forecasting of rainfall using ANN, GPS and meteorological data, International Conference for Convergence for Technology-2014, 6-8 April, Pune, India, pp. 1-4.
  15. Tran, Q.K. and Song, S.K. (2017), Water level forecasting based on deep learning: A use case of Trinity River-Texas-The United States, Journal of KIISE, Vol. 44, No. 6, pp. 607-612. https://doi.org/10.5626/JOK.2017.44.6.607

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