Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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On-line Identification of The Toxicological Substance in The Water System using Neural Network Technique

조류를 이용한 수계모니터링 시스템에서 뉴럴 네트워크에 의한 실시간 독성물질 판단

  • Jung, Jonghyuk (Department of Mechanical and Information Engineering, University of Seoul) ;
  • Jung, Hakyu (Department of Mechanical and Information Engineering, University of Seoul) ;
  • Kwon, Wontae (Department of Mechanical and Information Engineering, University of Seoul)
  • 정종혁 (서울시립대학교 기계정보공학과) ;
  • 정하규 (서울시립대학교 기계정보공학과) ;
  • 권원태 (서울시립대학교 기계정보공학과)
  • Received : 2007.10.22
  • Accepted : 2007.12.13
  • Published : 2008.01.30
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Abstract

Biological and chemical sensors are the two most frequently used sensors to monitor the water resource. Chemical sensor is very accurate to pick up the types and to measure the concentration of the chemical substance. Drawback is that it works for just one type of chemical substance. Therefore a lot of expensive monitoring system needs to be installed to determine the safeness of the water, which costs too much expense. Biological sensor, on the contrary, can judge the degree of pollution of the water with just one monitoring system. However, it is not easy to figure out the type of contaminant with a biological sensor. In this study, an endeavor is made to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve (FIC) from a biological monitoring system. Wem-tox values are calculated from the amount of flourescence of contaminated and reference water. Curve fitting is executed to find the representative curve of the raw data of Wem-tox values. Then the curves are digitalized at the same interval to train the neural network model. Taguchi method is used to optimize the neural network model parameters. The optimized model shows a good capacity to figure out the toxicant from FIC.

Keywords

References

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