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

  • 제35권5호
  • /
  • Pages.423-430
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  • 2017
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  • 1598-4850(pISSN)
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  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
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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)
  • 투고 : 2017.10.10
  • 심사 : 2017.10.16
  • 발행 : 2017.10.31
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초록

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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참고문헌

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피인용 문헌

  1. Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning vol.10, pp.12, 2017, https://doi.org/10.3390/app10124335
  2. 딥러닝 기반 GNSS 천정방향 대류권 습윤지연 추정 연구 vol.39, pp.1, 2017, https://doi.org/10.7848/ksgpc.2021.39.1.23