기계학습 모형 기반 진해만 용존산소농도 및 빈산소수괴 발생 예측
Prediction in Dissolved Oxygen Concentration and Occurrence of Hypoxia Water Mass in Jinhae Bay Based on Machine Learning Model
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초록
본 연구에서는 진해만의 단일 정점 장기 모니터링 자료를 사용하여 LSTM 모형을 이용한 DO 농도 예측및 결정 트리 모형을 이용한 빈산소수괴 발생 예측 연구를 수행하였다. LSTM을 이용한 DO 농도 예측 결과, Hidden node의 수가 증가할수록 모형의 복잡도가 증가하여 많은 Epoch을 요구하는 모습을 보였으며, 예측 시간 간격이 증가할수록 긴 Sequence length에서 높은 정확도를 보였다. 결정 트리를 이용한 빈산소수괴 발생 예측 결과, 30 day 예측에서 빈산소수괴 미발생 예측 정확도는 6 6 .1%로 발생 예측 정확도의 37.5%보다 상대적으로 높게 나타났다. 이는 결정 트리 모형이 DO 농도를 관측값보다 고평가하여 나타난 결과로 판단된다.
Abstract
We carried out studies on prediction in concentration of dissolved oxygen (DO) with LSTM model and prediction in occurrence of hypoxia water mass (HWM) with decision tree. As results of study on prediction in DO concentration, a large number of Hidden node caused high complexity of model and required enough Epoch. And it was high accuracy in long Sequence length as prediction time step increased. The results of prediction in occurrence of HWM showed that the accuracy of nonHWM case was 66.1% in 30 day prediction, it was higher than 37.5% of HWM case. The reason is that the decision tree might overestimate DO concentration.
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