Korean Journal of Ecology and Environment (생태와환경)

The Korean Society of Limnology (한국수생태학회)
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Machine- and Deep Learning Modelling Trends for Predicting Harmful Cyanobacterial Cells and Associated Metabolites Concentration in Inland Freshwaters: Comparison of Algorithms, Input Variables, and Learning Data Number

담수 유해남조 세포수·대사물질 농도 예측을 위한 머신러닝과 딥러닝 모델링 연구동향: 알고리즘, 입력변수 및 학습 데이터 수 비교

  • Yongeun Park (School of Civil and Environmental Engineering, Konkuk University) ;
  • Jin Hwi Kim (School of Civil and Environmental Engineering, Konkuk University) ;
  • Hankyu Lee (Graduate School of Civil, Environmental and Plant Engineering, Konkuk University) ;
  • Seohyun Byeon (Graduate School of Civil, Environmental and Plant Engineering, Konkuk University) ;
  • Soon-Jin Hwang (Department of Environmental Health and Science, Konkuk University) ;
  • Jae-Ki Shin (Limnoecological Science Research Institute Korea (THE HANGANG))
  • 박용은 (건국대학교 사회환경공학부) ;
  • 김진휘 (건국대학교 사회환경공학부) ;
  • 이한규 (건국대학교 사회환경플랜트공학과) ;
  • 변서현 (건국대학교 사회환경플랜트공학과) ;
  • 황순진 (건국대학교 환경보건과학과) ;
  • 신재기 (수생태원 한강 (韓江))
  • Received : 2023.08.13
  • Accepted : 2023.10.01
  • Published : 2023.09.30
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Abstract

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

근래에 들어, 머신러닝과 딥러닝 모델은 다양한 수체 내 수질변화를 예측하기 위해 광범위하게 사용되고 있다. 특히, 담수호의 물 이용과 수생태계 건강성에 위협 요인으로 작용할 수 있는 유해남조의 발생을 예측하기 위해 많은 연구자들이 인공지능 모델을 활용하고 있다. 따라서, 본 연구에서는 최근까지 유해남조의 발생을 예측하기 위해 적용된 인공지능 모델링의 선행 연구들을 검토하였고, 딥러닝을 포함하여 머신러닝 모델을 이용한 이 분야 연구의 발전방향을 모색하고자 하였다. 먼저, Elsevier의 초록 인용 데이터베이스인 Scopus를 활용하여 체계적인 문헌 연구를 수행하였다. 주요 키워드를 이용하여 탐색 및 정리된 문헌들을 리뷰한 결과, 딥러닝 모델은 주로 남조 세포수 예측에만 사용되었고, 머신러닝 모델은 남조 세포수 이외에 microcystin, geosmin, 2-MIB와 같은 대사물질 예측에도 초점을 맞추고 있었다. 또한, 남조 세포수와 대사물질의 예측을 위해 활용된 입력변수들은 현저한 차이가 있었다. 남조의 대사물질을 예측하기 위해 딥러닝 모델이 적용된 바가 없었는데, 향후 빅데이터 구축을 통한 대사물질을 예측하는 연구가 필요할 것으로 사료된다.

Keywords

Acknowledgement

본 논문의 심사과정에서 세세한 검토와 코멘트를 해 주신 익명의 심사위원들께 감사드립니다.

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