The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

ABSTRACT
1. 서 론
2. 재료 및 실험방법
    2.1 모형 개요
    2.2 모형 구축
    2.3 입력 데이터 구축
    2.4 모형 성능 비교 검정
3. 결과 및 고찰
    3.1 탁도 예측 결과
    3.2 모형 성능 비교분석
    3.3 입력 자료의 측정 빈도 등을 고려한 모형의 선정
4. 결 론
References

• 박정수(국립한밭대학교 건설환경공학과) | Jungsu Park (Department of Civil and Environmental engineering, Hanbat National University) Corresponding author