Water for future (물과 미래)

Korea Water Resources Association (한국수자원학회)
권호 표지

Urban Flood Prediction by LSTM & Logistic Regression

LSTM 모형과 로지스틱 회귀를 통한 도시침수 예측

  • 김현일 (환경부 낙동강홍수통제소 예보통제과) ;
  • 김병현 (경북대학교 토목공학과) ;
  • 한건연 (경북대학교 방재연구소) ;
  • 이재영 (한국건설기술연구원)
  • Published : 2021.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

Keywords

References

  1. Mosavi, A, Ozturk, P. and Chaw KK (2018). Flood Prediction Using Machine Learning Models:Uterature Review. Water, Vol. 10, doi:10.3390/w10111536.
  2. Le, X.H., Ho, H.V., Lee, G.H., Jung, S.H. (2019). Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water, Vol 11, doi:10.3390/w11071387.
  3. Seoul Metropolitan City (SMC) (2015). Comprehensive Plan for Storm and Flood Damage Reduction. Korea, Vol. 1, Chapter 3, pp.374-375.
  4. Kim, H.L., Han, K, Y., Lee, J.Y. (2020) Prediction of Urban Flood Extent by LSTM Model and Logistic Regression, Journal of the Korean Society of Civil Engineers, Vol. 40, doi:10.12652/Ksce.2020.40.3.0285