Conclusion and Achievements

❏ Achievements

ㅇ Since the prediction using a physical model is established based on a well-established theory, it is widely used to predict properties of water quality such as water temperature, dissolved oxygen, total phosphorus, and total nitrogen. The prediction using the physical equation based on the law of conservation of mass is well suited for conservative substance.

However, there is a limitation in the prediction of algae cells since it is the activity of living organisms.

ㅇ Existing algal phenomena prediction studies have not directly predicted the number of harmful blue-green algae cells, which is the direct cause of algal phenomena. It is replaced by using the results of chlorophyll a concentration prediction.

ㅇ In this study, a deep learning algorithm based on recurrent neural networks was used as an alternative method to predict the number of harmful blue-green algae cells. It well predicted the increasing or decreasing patterns of algae and the occurrence of abnormal phenomena at the concurrent point.

❏ Limitations

ㅇ Only water quality, upstream water quality, water level, and meteorological information were used as input variables. These variables are already used in the physical model. Taking into account social variables such as population change and the benefits of deep learning analytics can be leveraged to a greater extent. Unstructured information such as satellite images can be additionally considered.

ㅇ There is a limitation in the amount of data. In this study, the model was studied using data from a total of 365 weekly data collections from 2007 to 2016, but this amount itself is not sufficient. Whenever new data are added, the predictive model should be updated to increase the prediction efficiency.

ㅇ There is a limitation due to the black-box characteristic. The detailed operational process of the prediction model cannot be clearly observed.

When implementing a policy, evidence is needed. The black-box characteristic of deep learning prediction models makes it difficult to provide clear evidence.

❏ Conclusions and suggestions

ㅇ Because it is very simple to perform predictions with the model that has already been established, it can be directly used as reference information for current algal bloom forecasts.

ㅇ Since predictions using deep learning models and physics-based models both have advantages and disadvantages, it is most desirable to integrate the two prediction methods. Based on the deep learning model, the physical model can be integrated by including the physical equation in the constraint of the objective function. Or, deep learning can be partially performed in the partial module of the physical model prediction.

Keywords: Water Quality, Algal Bloom, Water Management, Deep Learning

홍한움 (연구책임)

서울대학교 이학박사(통계학)

한국환경정책·평가연구원 부연구위원(현) hwhong@kei.re.kr

주요 연구실적

∙ Estimation of Error Correction Model with Measurement Errors (2020)

∙ 국가지속가능성 이행과제간 연관관계 분석방안 연구 (2019)

∙ Estimation of Cointegrated Models with Exogenous Variables (2014)

조을생

한국환경정책·평가연구원 연구위원(현) escho@kei.re.kr

강선아

한국환경정책·평가연구원 연구원(현) sakang@kei.re.kr

한국진

한국환경정책·평가연구원 선임전문원(현) kjhan@kei.re.kr

기후환경정책연구

2016년 2016-01 미래환경 전망 및 지속가능사회 비전설정 기반 구축 (조공장)

2020-16 지속가능성 확보를 위한 자원순환 성능 및 처리기반 적정성 평가 연구(I) (이소라)

2018년 2018-01 개발기본계획의 전략환경영향평가 운영의 성과분석 및 발전방향 연구 (사공희) Land-Cover Changes, Exemplified by the Korean Demilitarized Zone and Inner-German Green Belt (Part I) (김오석)

2017-13 ICT 발전트렌드에 대응하는 공간정보의 환경이슈 적용 체계 구축: 빅데이터 분석 과 위성영상 활용을

2020년 2020-01 지속가능성 정책 지원을 위한 환경용량 평가 체계 및 활용 연구 (이승준)

2018-04 환경부문 개헌의 법적 효과에 관한 연구 (한상운)

2016-06 환경분야 공적개발원조(ODA) 사업평가 지침 마련을 위한 연구 (조공장)

2020-08 기후변화 적응대책에서의 생태계기반 적응(Ecosystem-based Adaptation) 도입 방안 모색 (박진한) 2020-09 홍수총량제 도입 검토를 위한 기초연구: 홍수 유역분담제 시행 방안 검토 (이승수)

2019-10 삶의 만족도 지표를 활용한 미세먼지의 사회적 비용 추정 연구 (전호철)

2016-06 드론을 이용한 환경재난 사후대응 기술 및 연구동향 분석 연구 (손승우)

2020-06-03 [별책부록] 2020 국민환경의식조사 (김현노)

위한 발암성 대기오염물질의 현황 및 배출원 특성 분석 (김유미)

환경적 고찰 (박종윤)

2017-14-01 (총괄) 동아시아 환경공동체 성과 확산 (추장민)

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