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DOI : 10.17640/KSWST.2019.27.1.59 ,    Vol.27, No.1, 59 ~ 71, 2019
Title
Odor Compounds Forecasting in Daecheong Water Intake Station Using Machine Learning Models
이종수 Jong-soo Lee , 조주영 Ju-young Cho , 박수진 Su-jin Park , 정세채 Se-chae Jeong , 오은정 Eun-jeong Oh , 왕창근 Changkeun-wang , 강호 Ho-kang
Abstract
The contamination of source water by odor compounds are one of the problems related to the water quality management, especially in Daecheong Reservoir, South Korea issued an algal alert system anually. This study investigated the efficiencies of 4 machine learning models, including Multi-parameter Regression Analysis(MRA), Decision Tree(DT), Artificial Neural Network (ANN) and Random Forest (RF), for odor compounds forecasting(Geosmin, 2-MIB) in the Daecheong Water Intake Station, where supply water treatment plants to source water. The models based on input variables considered correlation between target output and water quality parameter and hydrologic·meteorological factors. The established models showed good results between observed and simulated values. For Geosmin models, ANN produced better forcasting results than others. RF showed the best results for 2-MIB models. These results and models are applied in work-site operations through the Daecheng Intergreted Water Quality Forecasting System since September, 2018.
Key Words
Artificial neural network, Machine Learning, Odor compounds forecasting, Random Forest
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