In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, at first the correlation analysis between Chl-a and water quality and quantity data was studied. we extracted ten factors of high importance for water quality and quantity data. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

ABSTRACT
1. 서 론
2. 연구방법
    2.1 조사지점 및 시기
    2.2 자료수집 및 데이터 set 구성
    2.3 분석방법
3. 결과 및 고찰
    3.1 수질과 수량 항목 상관관계분석 결과
    3.2 알고리즘 성능평가
    3.3 알고리즘의 조정 하이퍼 파라미터 적용
4. 결 론
References

• 이상민(부경대학교 환경공학과) | Sang-Min Lee (Department of Environmental Engineering, Pukyong National University)
• 김일규(부경대학교 환경공학과) | Il-Kyu Kim (Department of Environmental Engineering, Pukyong National University) Corresponding author