References
- Ahmad, S., Khan, I. H. and Parida, B. P.: Performance of stochastic approaches for forecasting river water quality. Wat. Res., 35, 4261-4266, 2001 https://doi.org/10.1016/S0043-1354(01)00167-1
- Hameed, T. Marino, M. A. and Cheema, M. N. : Time series modeling of channel transmission losses. Agricultural Water Management, 29, 283-298, 1996 https://doi.org/10.1016/0378-3774(95)01201-X
- Montanari, A. and Rosso, R. : Fractionally differ-enced ARIMA models applied to hydrologic time series(Identification, estimation, and simulation). Water Resources Research, 33, 1035-1044, 1997 https://doi.org/10.1029/97WR00043
- Law, R. : Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21, 331-340, 2000 https://doi.org/10.1016/S0261-5177(99)00067-9
- Kolehmainen, M., Martikainen, H. and Ruuskanen, J. : Neural networks and periodic components used in air quality forecasting, Atmospheric Environment, 35, 815-825, 2001 https://doi.org/10.1016/S1352-2310(00)00385-X
- Maier, H. R., Dandy, G. C., and Burch, M. D. : Use of artificial neural networks for modelling cyanobac-teria Anabaena spp. in the River Murray, South Aus-tralia. Ecological Modelling, 105, 257-272, 1998 https://doi.org/10.1016/S0304-3800(97)00161-0
- Luk, K. C., Ball, J. E. and Sharma, A. : A study of optimal model lag and spatial input to artificial neural network for rainfall forecasting. Journal of Hydrology, 227, 56-65, 2000 https://doi.org/10.1016/S0022-1694(99)00165-1
- See, L. and Abrahart, R. J. : Multi-model data fusion for hydrological forecasting. Computer & Geosciences, 27, 987-994, 2001 https://doi.org/10.1016/S0098-3004(00)00136-9
- Hwarng, H. B. and Ang, H. T. : A simple neural net-work for ARMA(p, q) time series. The International Journal of Management Science, 29, 319-333, 2001
- 환경부, 환경연감, 1990-2000
- http://www.me.go.kr/www/index.html
- Kung, S. Y. : Digital Neural Networks, Prentice Hall International Inc. 30-33, 1993
- Balkin, S. D. and Ord, J. K. : Automatic neural network modelling for univariate time series. International Journal of Forecasting, 16, 509-515, 2000 https://doi.org/10.1016/S0169-2070(00)00072-8