This study develops a model to determine the input rate of the chemical for coagulation and flocculation process (i.e. coagulant) at industrial water treatment plant, based on real-world data. To detect outliers among the collected data, a two-phase algorithm with standardization transformation and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied. In addition, both of the missing data and outliers are revised with linear interpolation. To determine the coagulant rate, various kinds of machine learning models are tested as well as linear regression. Among them, the random forest model with min-max scaled data provides the best performance, whose MSE, MAPE, R2 and CVRMSE are 1.136, 0.111, 0.912, and 18.704, respectively. This study demonstrates the practical applicability of machine learning based chemical input decision model, which can lead to a smart management and response systems for clean and safe water treatment plant.

1. 서 론
2. 선행 연구
3. 정수처리 프로세스 및 데이터 수집
    3.1 정수처리 프로세스
    3.2 데이터 구성 및 특징
4. 결측치 및 이상치 처리 프로세스
    4.1 결측치 처리
    4.2 이상치 처리
5. 응집제 투입률 예측
    5.1 상관분석
    5.2 데이터 학습 모델
    5.3 예측 결과
Acknowledgement
References
6. 결 론

• Kyungsu Park(Department of Business Administration, Pusan National University) | 박경수 (부산대학교 경영학과)
• Yu-jin Lee(Department of Business Administration, Pusan National University) | 이유진 (부산대학교 경영학과)
• Haneul Noh(Department of Business Administration, Pusan National University) | 노하늘 (부산대학교 경영학과)
• Jun Heo(Korea Water Resources Corporation) | 허준 (한국수자원공사)
• Seung Hwan Jung(School of Business, Yonsei University) | 정승환 (연세대학교 경영학과) Corresponding author