A Study on the Turbidity Estimation Model Using Data Mining Techniques in the Water Supply System

Park, No-Suk; Kim, Soonho; Lee, Young Joo; Yoon, Sukmin; Park, No-Suk; Kim, Soonho; Lee, Young Joo; Yoon, Sukmin

doi:2016.38.2.87

The Korean text of this paper can be translated into multiple languages on the website of http://jksee.or.kr through Google Translator.

J Korean Soc Environ Eng > Volume 38(2); 2016 > Article

Original Paper

J Korean Soc Environ Eng 2016;38(2): 87-95. doi: https://doi.org/10.4491/KSEE.2016.38.2.87

데이터마이닝 기법을 이용한 상수도 시스템 내의 탁도 예측모형 개발에 관한 연구

박노석¹, 김순호¹, 이영주², 윤석민¹

¹경상대학교 토목공학과 및 공학연구원
²K-water 연구원

A Study on the Turbidity Estimation Model Using Data Mining Techniques in the Water Supply System

No-Suk Park¹, Soonho Kim¹, Young Joo Lee², Sukmin Yoon¹

¹Department of Civil Engineering and Engineering Research Institute, Gyeongsang National University
²K-water Institute

Corresponding author

Sukmin Yoon ,Tel: 055-755-8707, Fax: 055-772-1799, Email: gnuysm@gmail.com

Received: January 27, 2016; Revised: February 18, 2016; Accepted: February 23, 2016. Published online: February 29, 2016.

ABSTRACT

Turbidity is a key indicator to the user that the 'Discolored Water' phenomenon known to be caused by corrosion of the pipeline in the water supply system. 'Discolored Water' is defined as a state with a turbidity of the degree to which the user visually be able to recognize water. Therefore, this study used data mining techniques in order to estimate turbidity changes in water supply system. Decision tree analysis was applied in data mining techniques to develop estimation models for turbidity changes in the water supply system. The pH and residual chlorine dataset was used as variables of the turbidity estimation model. As a result, the case of applying both variables(pH and residual chlorine) were shown more reasonable estimation results than models only using each variable. However, the estimation model developed in this study were shown to have underestimated predictions for the peak observed values. To overcome this disadvantage, a high-pass filter method was introduced as a pre-treatment of estimation model. Modified model using high-pass filter method showed more exactly predictions for the peak observed values as well as improved prediction performance than the conventional model.

Key Words: Turbidity, Discolored Water, Data Mining Techniques, Decision Tree Analysis