Journal of Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회논문집)

Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회)
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Prediction in Dissolved Oxygen Concentration and Occurrence of Hypoxia Water Mass in Jinhae Bay Based on Machine Learning Model

기계학습 모형 기반 진해만 용존산소농도 및 빈산소수괴 발생 예측

  • Park, Seongsik (Department of Ocean Engineering, Pukyong National University) ;
  • Kim, Byeong Kuk (Tongyeong Terminal Division, Korea Gas Corporation) ;
  • Kim, Kyunghoi (Department of Ocean Engineering, Pukyong National University)
  • 박성식 (부경대학교 해양공학과) ;
  • 김병국 (한국가스공사 안전환경부) ;
  • 김경회 (부경대학교 해양공학과)
  • Received : 2022.04.28
  • Accepted : 2022.06.07
  • Published : 2022.06.30
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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.

본 연구에서는 진해만의 단일 정점 장기 모니터링 자료를 사용하여 LSTM 모형을 이용한 DO 농도 예측 및 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.

Keywords

Acknowledgement

이 논문은 2022년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(Grant 2021R1I1A30603741261782064340102).

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