Journal of Korean Society of Water and Wastewater (상하수도학회지)

The Korean Society of Water and Wastewater (대한상하수도학회)
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A study on applying random forest and gradient boosting algorithm for Chl-a prediction of Daecheong lake

대청호 Chl-a 예측을 위한 random forest와 gradient boosting 알고리즘 적용 연구

  • Lee, Sang-Min (Department of Environmental Engineering, Pukyong National University) ;
  • Kim, Il-Kyu (Department of Environmental Engineering, Pukyong National University)
  • 이상민 (부경대학교 환경공학과) ;
  • 김일규 (부경대학교 환경공학과)
  • Received : 2021.10.26
  • Accepted : 2021.12.14
  • Published : 2021.12.15
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Abstract

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.

Keywords

Acknowledgement

본 연구는 "부경대학교 자율창의학술연구비 지원사업(2021)"의 일환으로 수행되었음.

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