[논문]Attention 기법을 적용한 LSTM-s2s 모델 기반 댐유입량 예측 연구

이종혁; 최수연; 김연주

doi:10.3741/jkwra.2022.55.7.495

Attention 기법을 적용한 LSTM-s2s 모델 기반 댐유입량 예측 연구
Prediction of dam inflow based on LSTM-s2s model using luong attention 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.55 no.7, 2022년, pp.495 - 504

이종혁 (연세대학교 건설환경공학과) , 최수연 (연세대학교 건설환경공학과) , 김연주 (연세대학교 건설환경공학과)

초록
AI-Helper

최근 인공지능의 발전으로 시계열 자료 분석에 효과적인 Long Short-Term Memory (LSTM) 모델이 댐유입량 예측의 정확도를 높이는 데 활용되고 있다. 본 연구에서는 그 중 LSTM의 성능을 더욱 향상할 수 있는 Sequence-to-Sequence (s2s) 구조에 Attention 기법을 LSTM 모델에 첨가하여 소양강댐 유역의 유입량을 예측하였다. 분석 데이터는 2013년부터 2020년까지의 유입량 시자료와 종관기상관측기온 및 강수량 자료를 학습, 검증, 평가로 나누어 훈련한 후, 모델의 성능 평가를 진행하였다. 분석 결과, LSTM-s2s 모델보다 attention까지 첨가한 모델이 일반적으로 더 좋은 성능을 보였으며, attention 첨가 모델이 첨두값도 더 잘 예측하는 모습을 보였다. 그리고 두 모델 모두 첨두값 발생 동안 유량 패턴을 잘 반영하였지만 세밀한 시간 단위 변화량에는 어려움이 있었다. 이를 통해 시간 단위 예측의 어려움에도 불구하고, LSTM-s2s에 attention까지 첨가한 모델이 기존 LSTM-s2s의 예측 성능을 향상할 수 있음을 알 수 있었다.

Abstract ▼ AI-Helper

With the recent development of artificial intelligence, a Long Short-Term Memory (LSTM) model that is efficient with time-series analysis is being used to increase the accuracy of predicting the inflow of dams. In this study, we predict the inflow of the Soyang River dam, using the LSTM model with the Sequence-to-Sequence (LSTM-s2s) and attention mechanism (LSTM-s2s with attention) that can further improve the LSTM performance. Hourly inflow, temperature, and precipitation data from 2013 to 2020 were used to train the model, and validate and test for evaluating the performance of the models. As a result, the LSTM-s2s with attention showed better performance than the LSTM-s2s in general as well as in predicting a peak value. Both models captured the inflow pattern during the peaks but detailed hourly variability is limitedly simulated. We conclude that the proposed LSTM-s2s with attention can improve inflow forecasting despite its limits in hourly prediction.

주제어

표/그림 (6)

그림 Fig. 1. Conceptual diagram of the LSTM-based s2s model with m time step input and n time step output reproduced from xiang (Xiang et al, 2020)
그림 Fig. 2. Conceptual diagram of the LSTM-Attention model with m time step input and t time step output
그림 Fig. 3. Study area of this study; Soyang River dam watershed
그림 Fig. 4. R², RRMSE, CC, NSE, and PBIAS evaluation results for each input sequence and prediction hour from the LSTM-s2s and LSTM-s2s with attention added
표 Table 1. Summary of evaluation result for 8h input sequence and prediction hour from the LSTM-s2s and LSTM-s2s with attention added
그림 Fig. 5. Comparison of true and predicted inflows obtained from the LSTM-s2s and LSTM-s2s with attention added at test stage with max inflow in each year. (a) and (b) indicate when the max inflow happens in 2019 (2019.07.25. - 2019.08.02) and 2020 (2020.08.01. - 2020.08.09.), respectively

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