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Water Quality Forecasting of Chungju Lake Using Artificial Neural Network Algorithm

인공신경망 이론을 이용한 충주호의 수질예측

  • 정효준 (서울대학교 보건대학원 환경보건학과) ;
  • 이소진 (서울대학교 보건대학원 환경보건학과) ;
  • 이홍근 (서울대학교 보건대학원 환경보건학과)
  • Published : 2002.03.01

Abstract

This study was carried out to evaluate the artificial neural network algorithm for water quality forecasting in Chungju lake, north Chungcheong province. Multi-layer perceptron(MLP) was used to train artificial neural networks. MLP was composed of one input layer, two hidden layers and one output layer. Transfer functions of the hidden layer were sigmoid and linear function. The number of node in the hidden layer was decided by trial and error method. It showed that appropriate node number in the hidden layer is 10 for pH training, 15 for DO and BOD, respectively. Reliability index was used to verify for the forecasting power. Considering some outlying data, artificial neural network fitted well between actual water quality data and computed data by artificial neural networks.

Keywords

References

  1. Law, R., 2000, Back-propagation learning in improving the accuracy of neural network based tourism demand forecasting, Tourism Management 21, 331-340 https://doi.org/10.1016/S0261-5177(99)00067-9
  2. Kolehmainen, M., H. Martikainen and J. Ruuskanen, 2001, Neura1 networks and periodic components used in air quality forecasting, Atmospheric Environment, 35, 815-82 https://doi.org/10.1016/S1352-2310(00)00385-X
  3. Maier, H. R., G. C. Dandy, and M. D. Burch, 1998, Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia, Ecological Modelling, 105, 257-272 https://doi.org/10.1016/S0304-3800(97)00161-0
  4. Luk, K. C., J. E. Ball, and A. Sharma, 2000, A study of optimal model lag and spatial input to artificial neural network for rainfall forecasting, Journal of Hydrology, 227, 56-65 https://doi.org/10.1016/S0022-1694(99)00165-1
  5. See, L. and R. J. Abrahart, 2001, Multi-model data fusion for hydrological forecasting, Computer & Geosciences, 27, 987-994 https://doi.org/10.1016/S0098-3004(00)00136-9
  6. Hwarng, H. B. and H. T. Ang, 2001, A simple neural network for ARMA(p, q) time series, The International Journal of Management Science, 29, 319-333
  7. Skapura, D. M., 1996, Building Neural Networks, Addison Wesley, 1-5
  8. 김대수, 1999, 신경망이론과 응용(I), 하이테크정보 92pp
  9. 환경부, 1991-2000, 환경연감
  10. http://www.me.go.kr/www/index.htrru
  11. Kung, 1993, Digital Neural Networks, Prentice Hall International Inc, 30-33 pp
  12. Balkin, S. D. and J. K. Ord, 2000, Automatic neural network modelling for univariate time series, International Journal of Forecasting, 16, 509-515 https://doi.org/10.1016/S0169-2070(00)00072-8
  13. 환경부, 1995, 수역 수질관리를 위한 수질예측 모형과 의사결정 지원시스템 개발에 관한 연구,84pp.