물과 미래 (Water for future)

한국수자원학회 (Korea Water Resources Association)
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인공신경망 딥러닝 기반 레이더 강우예측 연구 현황

  • 윤성심 (한국건설기술연구원 국토보전연구본부) ;
  • 이동률 (한국건설기술연구원 국토보전연구본부)
  • 발행 : 2018.09.15
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초록

키워드

참고문헌

  1. 마쓰오 유타카 (2015) 인공지능과 딥러닝, (주)동아엠앤비.
  2. Kim, J., Pyo, H., Ha, J., Lee, C., Kim, J. (2015) "Deep learning algorithms and applications" Communications of the Korean Institute of Information Scientists and Engineers, Korea Information Science Society, 33(8), 2015.8, 25-31.
  3. Kim, S., Hong, S., Joh, M., Song, S. (2017) "DEEPRAIN: convLSTM network for precipitation prediction using multichannel radar data" 7th International Workshop on Climater Informatict September 20-22.
  4. Ha, J.H., Lee, Y.H., Kim, Y.H. (2016). "Forecasting the Precipitation of the Next Day Using Deep Learning", Journal of Korean Institute of Intelligent Systems 26(2), 93-98. https://doi.org/10.5391/JKIIS.2016.26.2.093
  5. Hall, T., Brooks, H.E., Doswell, C. A. (1999) "Precipitation forecasting using a neural network", Weather and Forecasting, vol. 14, no.3, pp. 338-345, 1999. https://doi.org/10.1175/1520-0434(1999)014<0338:PFUANN>2.0.CO;2
  6. Kuligowski, R.J., Barros, A.P. (1998), "Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks", Weather and Forecasting, vol. 13, no. 4, pp.1194-1204 https://doi.org/10.1175/1520-0434(1998)013<1194:LPFFAN>2.0.CO;2
  7. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P. (1998) "Gradient-based learning applied to document recognition" Proceedings of the IEEE, vol. 86, issue 11, pp. 2278-2324, November 1998. https://doi.org/10.1109/5.726791
  8. Lee, S., Cho, S., Wong, P.M. (1998), "Rainfall prediction using artificial neural networks", Journal of Geographic Information and Decision Analysis, vol. 2, no. 2, pp. 233-242
  9. Nguyen, M., Nguyen, P., Vo, T., Hoang, L. (2017) "Deep Neural Networks with Residual Connections for Precipitation Forecasting" CIKM 2017, Nov 2017, Singapore
  10. Seo, J.H., Lee, Y.H., Kim, Y.H. (2012), "Feature selection to predict very short-term heavy rainfall based on differential evolution", Journal of Korean Institute of Intelligent Systems, vol. 22, no. 6, pp. 706-714. https://doi.org/10.5391/JKIIS.2012.22.6.706
  11. Shenzhen Meteorological Bureau-Alibab, "Short-Term Quantitative Precipitation Forecasting," https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.jsxLyX&raceId=231596
  12. Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., Woo, W. (2015) "Convolutional LSTM network: A machine learning approach for precipitation nowcasting." Advances in Neural Information Processing Systems. 2015.
  13. Shi X., Gao, Z., Lausen, L., Wang, H., Yeung D., Wong, W., Woo, W. (2017) " Deep learning for precipitation nowcasting: a benchmark and a new model" 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
  14. Wong, W., Shi, X., Yeung, D., Woo, W. (2016). "A deep-learning method for precipitation nowcasting" WMO WWRP 4th International symposium on nowcasting and very-short-range forecast 2016.
  15. Yao, Y., Li, Z. (2017) "CIKM AnalytiCup 2017: Short-Term Precipitation Forecasting Based on Radar Reflectivity Images" CIKM 2017, Nov 2017, Singapore.
  16. Zhang, Z., Wei, S. (2017) "A Method for Short-Term Quantitative Precipitation Forecasting" CIKM 2017, Nov 2017, Singapore.