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담수 유해남조 세포수ㆍ대사물질 농도 예측을 위한 머신러닝과 딥러닝 모델링 연구동향: 알고리즘, 입력변수 및 학습 데이터 수 비교 KCI 등재
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
pp.268-279
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.
재료 및 방 법
1. 국내외 문헌 데이터 수집
2. 탐색 문헌 데이터 분석
3. 텍스트마이닝 및 동시 출현단어 분석
결과 및 고 찰
1. CHABs 세포수와 대사물질 농도 예측을 위한ML과 DL 모델링의 선행 연구현황 및 네트워크 분석
2. CHABs 세포수와 대사물질 농도 예측을 위한ML과 DL 모델에 적용된 알고리즘 비교
3. CHABs 세포수와 대사물질 농도 예측을 위한ML과 DL 모델링에 적용된 입력변수 비교
4. CHABs 세포수와 대사물질 농도 예측을 위한ML과 DL 모델링에 사용된 학습 데이터 수 비교
결 론
적 요
