[논문]머신러닝과 딥러닝을 이용한 저수지 유해 남조류 발생 예측

김상훈; 박준형; 김병현

doi:10.3741/jkwra.2021.54.s-1.1167

머신러닝과 딥러닝을 이용한 저수지 유해 남조류 발생 예측
Prediction of cyanobacteria harmful algal blooms in reservoir using machine learning and deep learning 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.12 suppl., 2021년, pp.1167 - 1181

김상훈 (한국수자원공사 보현산댐지사) , 박준형 (행정안전부 국가민방위재난안전교육원) , 김병현 (경북대학교 토목공학과)

초록
AI-Helper

녹조 현상과 관련하여 독성물질을 배출하는 남조류 4종의 경우 유해 남조류로 지정하여 관리하고 있으며, 물리적인 모형을 이용한 예측 정보도 함께 발표하고 있다. 그러나 조류는 살아 있는 생명체로 물리 역학에 따른 예측에 어려움이 있으며, 기상, 수리·수문, 수질 등 수많은 인자에 의한 영향을 고려하기가 쉽지 않다. 따라서, 최근 머신러닝을 이용한 녹조발생 예측 연구가 많이 진행되고 있다. 본 연구에서는 경북 영천에 소재한 보현산댐과 영천댐을 대상으로 랜덤 포레스트 모형을 이용하여 유해남조류 발생에 영향을 미치는 수질인자의 특성중요도를 분석해 보았으며, 이 중 가장 높은 특성중요도를 나타낸 수온을 이용하여 머신러닝과 딥러닝을 이용하여 유해남조류 발생을 예측하고 그 정확성을 확인하였다. 특성중요도 분석 결과, 수온과 총질소(T-N)이 공통적으로 높게 나왔으며, 인공신경망(ANN)을 이용한 유해남조류 발생예측에서도 실제와 근접한 값이 예측되어 앞으로 녹조관리를 위해 유해남조류 예측이 필요한 저수지의 경우 이를 활용할 수 있음을 확인하였다.

Abstract ▼ AI-Helper

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.

주제어

표/그림 (19)

그림 Fig. 1. Architecture of recurrent neural network and functions
그림 Fig. 2. Architecture of (a) LSTM and (b) GRU
그림 Fig. 3. Location of Bohyunsan Dam and Yeongcheon Dam
그림 Fig. 4. Bohyeonsan Dam water environment improvement comprehensive measures
그림 Fig. 5. Records and operating point of the Yeongcheon Dam algae alarm system since 2004
그림 Fig. 6. Distribution of water quality factors in Bohyunsan Dam and Yeongcheon Dam
그림 Fig. 7. Correlation analysis of water quality characteristics of Bohyunsan Dam and Yeongcheon Dam using heat map (15 items)
그림 Fig. 8. Correlation analysis of water quality characteristics of Bohyunsan Dam and Yeongcheon Dam using a scatter plot
그림 Fig. 9. Analysis of characteristic significance of harmful cyanobacteria in Bohyunsan Dam and Yeongcheon Dam
그림 Fig. 10. Random forest tree
그림 Fig. 11. Changes in time series of hydrology, weather, and water quality related to harmful cyanobacteria at Yeongcheon Dam in the summer of 2019
그림 Fig. 12. Changes in time series of hydrology, weather, and water quality related to harmful cyanobacteria at Yeongcheon Dam in the summer of 2020
그림 Fig. 13. CyanoHABs in Yeongcheon Dam (2004~2020)
그림 Fig. 14. CyanoHABs prediction using ANN model
그림 Fig. 15. CyanoHABs prediction using RNN model
그림 Fig. 16. CyanoHABs prediction using LSTM model
그림 Fig. 17. CyanoHABs prediction using GRU model
그림 Fig. 18. Accuracy evaluation using training and test data
표 Table 1. Accuracy evaluation of each machine and deep learning models

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