[논문]Sequence to Sequence based LSTM (LSTM-s2s)모형을 이용한 댐유입량 예측에 대한 연구

한희찬; 최창현; 정재원; 김형수

doi:10.3741/jkwra.2021.54.3.157

Sequence to Sequence based LSTM (LSTM-s2s)모형을 이용한 댐유입량 예측에 대한 연구
Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.3, 2021년, pp.157 - 166

한희찬 (콜로라도 주립 대학교 토목환경공학과) , 최창현 (KB손해사정 위험관리실) , 정재원 (인하대학교 수자원시스템연구소) , 김형수 (인하대학교 사회인프라공학과)

초록
AI-Helper

효율적인 댐 운영을 위해서는 높은 신뢰도를 기반으로 하는 유입량 예측이 요구된다. 본 연구에서는 최근 다양한 분야에서 사용되고 있는 데이터 기반의 예측 방법 중 하나인 딥러닝을 댐 유입량 예측에 활용하였다. 그 중 시계열 자료 예측에 높은 성능을 보이는 Sequence-to-Sequence 구조기반의 Long Short-Term Memory 딥러닝 모형(LSTM-s2s)을 이용하여 소양강 댐의 유입량을 예측하였다. 모형의 예측 성능을 평가하기 위해 상관계수, Nash-Sutcliffe 효율계수, 평균편차비율, 그리고 첨두값 오차를 이용하였다. 그 결과, LSTM-s2s 모형은 댐 유입량 예측에 대한 높은 정확도를 보였으며, 단일 유량 수문곡선 기반의 예측 성능에서도 높은 신뢰도를 보였다. 이를 통해 홍수기와 이수기에 수자원 관리를 위한 효율적인 댐 운영에 딥러닝 모형의 적용 가능성을 확인할 수 있었다.

Abstract ▼ AI-Helper

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

주제어

표/그림 (6)

그림 Fig. 1. Study area of this study; Soyang River dam watershed
그림 Fig. 2. Conceptual diagram of LSTM cells and LSTM-s2s model
그림 Fig. 3. Optimization results of different parameters. Each plot shows model performances using three evaluation metrics. (a) ‒ (d) indicate evaluation results for different four hyperparameters, including sequence length, number of layers, batch size, and epoch size
그림 Fig. 4. Comparison of observed and predicted inflow with lead time of 1 day from LSTM-s2s model, (a) time-series of observed and predicted inflows and (b) scatter plots of two inflows. Blue rectangle represents a scatter plot for inflows ranged between 0 ‒ 500 cms
표 Table 1. Evaluation results for wet and dry seasons
그림 Fig. 5. Observed and predicted hydrographs from LSTM-s2s model at the validation stage in 6 runoff events

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