[논문]실시간 댐 유입량 예측을 위한 인공신경망 모형의 활용성 평가

허재영; 배덕효

doi:10.3741/jkwra.2021.54.s-1.1131

[국내논문] 실시간 댐 유입량 예측을 위한 인공신경망 모형의 활용성 평가
Assessment of artificial neural network model for real-time dam inflow prediction 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.12 suppl., 2021년, pp.1131 - 1141

허재영 (세종대학교 건설환경공학과) , 배덕효 (세종대학교 건설환경공학과)

초록
AI-Helper

본 연구에서는 국내 주요 댐 상류 유역을 대상으로 장기간 시단위 수문자료를 활용하여 실시간 댐 유입량 예측을 위한 인공신경망 모형의 선행 1, 3, 6시간별 예측유입량을 산정 및 평가하였다. 이를 위해, 각 유역별 15년의 시단위 강수 및 유입량 자료를 활용하였으며 연도별 평균 유입량을 고려하여 데이터세트를 구성하였다. 각 대상유역에 대한 선행시간별 예측 성능은 NSE 0.57~0.79 이상으로써 비교적 양호한 성능을 나타내었다. 유역면적이 클수록 이수기의 예측 성능이 낮은 것으로 확인되었으며 홍수기 예측성능과의 편차가 증가하는 것으로 확인되었다. 선행 6시간 예측에 대해 입력자료의 과거길이에 따른 성능 변화는 홍수기보다 이수기에서 큰 차이를 보이며 과거길이가 증가할수록 이수기의 성능이 향상되는 것으로 나타났다. 주요 홍수 사상에 대한 예측 수문곡선은 관측과 비교하여 수문곡선의 형태는 유사한 것으로 나타났다. 다만, 선행시간에 따라 첨두시간의 지연 및 유량의 과소 추정되는 경향이 있으며 이에 대한 개선이 필요함을 확인하였다.

Abstract ▼ AI-Helper

In this study, the artificial neural network model is applied for real-time dam inflow prediction and then evaluated for the prediction lead times (1, 3, 6 hr) in dam basins in Korea. For the training and testing the model, hourly precipitation and inflow are used as input data according to average annual inflow. The results show that the model performance for up to 6 hour is acceptable because the NSE is 0.57 to 0.79 or higher. Totally, the predictive performance of the model in dry seasons is weaker than the performance in wet seasons, and this difference in performance increases in the larger basin. For the 6 hour prediction lead time, the model performance changes as the sequence length increases. These changes are significant for the dry season with increasing sequence length compared to the wet season. Also, with increasing the sequence length, the prediction performance of the model improved during the dry season. Comparison of observed and predicted hydrographs for flood events showed that although the shape of the prediction hydrograph is similar to the observed hydrograph, the peak flow tends to be underestimated and the peak time is delayed depending on the prediction lead time.

주제어

표/그림 (7)

그림 Fig. 1. Structure of artificial neural network
그림 Fig. 2. Forward and backward calculation in artificial neural network
그림 Fig. 3. Study area
표 Table 1. Results of model prediction performance with single-step-ahead and multi-step-ahead
그림 Fig. 4. Comparison of observed and predicted inflow of wet and dry season
그림 Fig. 5. Results of prediction performance according to input sequence length
그림 Fig. 6. Observed and predicted hydrographs for each events

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