A Study on the Statistical Predictability of Drinking Water Qualities for Contamination Warning System

수질오염 감시체계 구축을 위한 수질 데이터의 통계적 예측 가능성 검토

Park, No-Suk;Lee, Young-Joo;Chae, Seonha;Yoon, Sukmin

  • Received : 2015.05.29
  • Accepted : 2015.08.05
  • Published : 2015.08.15


This study have been conducted to analyze the feasibility of establishing Contamination Warning System(CWS) that is capable of monitoring early natural or intentional water quality accidents, and providing active and quick responses for domestic C_water supply system. In order to evaluate the water quality data set, pH, turbidity and free residual chlorine concentration data were collected and each statistical value(mean, variation, range) was calculated, then the seasonal variability of those were analyzed using the independent t-test. From the results of analyzing the distribution of outliers in the measurement data using a high-pass filter, it could be confirmed that a lot of lower outliers appeared due to data missing. In addition, linear filter model based on autoregressive model(AR(1) and AR(2)) was applied for the state estimation of each water quality data set. From the results of analyzing the variability of the autocorrelation coefficient structure according to the change of window size(6hours~48hours), at least the window size longer than 12hours should be necessary for estimating the state of water quality data satisfactorily.


contamination warning system;outlier;high-pass filter;linear filter model


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Cited by

  1. A Study on the Turbidity Estimation Model Using Data Mining Techniques in the Water Supply System vol.38, pp.2, 2016,
  2. Application of the LPCF Model for Real-Time Contamination Warning in the Water Supply System vol.40, pp.10, 2018,


Grant : 차세대 에코이노베이션사업(글로벌탑 환경기술개발사업)

Supported by : 환경부