수문분야에서의 딥러닝 적용 사례

  • 정성호 (경북대학교 미래과학기술융합학과) ;
  • 이대업 (경북대학교 재난대응전략연구소) ;
  • ;
  • 연민호 (경북대학교 미래과학기술융합학과) ;
  • 이기하 (경북대학교 미래과학기술융합학과)
  • Published : 2021.06.30

Abstract

Keywords

References

  1. 박해선. (2020). "혼자 공부하는 머신러닝+딥러닝.", 한빛미디어.
  2. Immerzeel, W. (2010). "Bias correction for satellite precipitation estimation used by the MRC Mekong flood forecasting system." FutureWater Report, 94.
  3. Jung, S., Cho, H., Kim, J., Lee, G. (2018). "Prediction of water level ina tidal river using a deep-learning based LSTM model." Journal of Korea Water Resources Association, Vol. 51, No. 12, pp. 1207-1216.
  4. Karimpouli S., Tahmasebi P. (2019). "Segmentation of digital rock images using deep convolutional autoencoder networks." Computers and Geosciences, Vol. 126, pp. 142-150. https://doi.org/10.1016/j.cageo.2019.02.003
  5. Le X.H., Lee G.H., Jung K.S., An H.U., and Lee S.S. (2020). "Application of Convolutional Neural network for Spatiotemporal Bias Correction of Daily Satellite-Based Precipitation." Remote Sensing, Vol. 12, No. 17, pp. 2731. https://doi.org/10.3390/rs12172731
  6. Lee, G. H., Jung, S. H., and Lee, D. E. (2018). "Comparison of physicsbased and data-driven models for streamflow simulation of the Mekong river." Journal of Korea Water Resources Association, Vol. 51, No. 6, pp. 503-514. https://doi.org/10.3741/JKWRA.2018.51.6.503
  7. Masci J., Meier U., Ciresan D., and Schmidhuber, J. (2011). "Stacked convolutional auto-encoders for hierarchical feature extraction." International conference on artificial neural networks, Springer, Berlin, Heidelberg, pp. 52-59.
  8. Park N.J. and Ko H.S. (2020). "Agglomerative Hierarchical Clustering Analysis with Deep Convolutional Autoencoders." Journal of Korea Multimedia Society, Vol. 23, No. 1, pp. 1-7.
  9. Sit, M., Demiray, B. Z., Xiang, Z., Ewing, G. J., Sermet, Y., and Demir, I. (2020). "A comprehensive review of deep learning applications in hydrology and water resources." Water Science and Technology, Vol. 82, No. 12, pp. 2635-2670. https://doi.org/10.2166/wst.2020.369