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2024 Vol.57, Issue 7 Preview Page

Research Article

Utilizing deep learning algorithm and high-resolution precipitation product to predict water level variability

고해상도 강우자료와 딥러닝 알고리즘을 활용한 수위 변동성 예측

Heechan Hana
,
Narae Kangb
,
Jungsoo Yoonc
,
Seokhwan Hwangd*
한 희찬a
,
강 나래b
,
윤 정수c
,
황 석환d*
aAssistant Professor, Department of Civil Engineering, Chosun University, Gwangju, Korea
bSenior Researcher, Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
cSenior Researcher, Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
dResearch Fellow, Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
a조선대학교 토목공학과 조교수
b한국건설기술연구원 수자원하천연구본부 수석연구원
c한국건설기술연구원 수자원하천연구본부 수석연구원
d한국건설기술연구원 수자원하천연구본부 연구위원
*Corresponding Author
31 July 2024. pp. 471-479
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Abstract

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.

Keywords
CMORPH
Deep learning
LSTM algorithm
Seomjin River
Water level variability prediction

기후변화로 인한 집중호우의 발생으로 홍수 피해가 심각해지고 있다. 하천의 수위 변동성을 예측하고 신속한 홍수 예·경보를 위해 물리적 기반의 수문 모형이 활용됐다. 최근에는 수문 데이터 간의 비선형적인 관계를 기반으로 머신러닝, 딥러닝 알고리즘을 활용한 수문 모의가 주목받고 있다. 본 연구에서는 Long Short-Term Memory (LSTM) 알고리즘을 활용하여 섬진강 수계의 하천 수위를 예측하고자 한다. 또한 Climate Prediction Center morphing method (CMORPH) 기반의 격자형 강우 자료를 알고리즘의 입력자료로 적용하여 지상 데이터의 한계를 보완하고자 한다. CMORPH 데이터와 LSTM 알고리즘을 결합한 모형의 수위 예측 결과는 평균 CC가 0.98, RMSE는 0.07 m, 그리고 NSE는 0.97로 나타났다. 향후 딥러닝과 원격자료를 활용하여 수위 예측을 수행한다면 지상 관측 데이터의 단점을 보완하고, 신뢰도 높은 예측 결과를 얻을 수 있을 것으로 기대되는 바이다.

키워드
CMORPH
딥러닝
LSTM 알고리즘
섬진강
수위 변동성 예측
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 57
  • No :7
  • Pages :471-479
  • Received Date : 2024-04-04
  • Revised Date : 2024-06-21
  • Accepted Date : 2024-06-25
  • DOI :https://doi.org/10.3741/JKWRA.2024.57.7.471
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