참고문헌
- Almasri, M. N. and Kaluarachchi, J. J., 2005, Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrate loading and recharge data. Environmental Modelling and Software, 20, 851-871. https://doi.org/10.1016/j.envsoft.2004.05.001
- Asefa, T., Kemblowski, M., McKee, M., and Khalil, A., 2006, Multi-time scale stream flow predictions: The support vector machines approach. Journal of Hydrology, 318, 7-16. https://doi.org/10.1016/j.jhydrol.2005.06.001
- Box, G. E. P. and Jenkins, G. M., 1976, Time Series Analysis- Forecasting and Control, Holden-Day, San Francisco, California, USA, 575p.
- Coppola, E., Rana, A. J., Poulton, M. M., Szidarovszky, F., and Uhl, V. V., 2005, A neural network model for predicting aquifer water level elevations, Ground Water 43(2), 231-241. https://doi.org/10.1111/j.1745-6584.2005.0003.x
- Hsu, K. L., Gupta, H. V., Gao, X. G., Sorooshian, S., and Imam, B., 2002, Self-organizing linear output map (SOLO): an artificial neural network suitable for hydrologic modeling and analysis, Water Resources Research, 38(12), 381-3817. https://doi.org/10.1029/2001WR001058
- Jain, A. and Kumar, A. M., 2007, Hybrid neural network models for hydrologic time series forecasting, Applied Soft Computing, 7, 585-592. https://doi.org/10.1016/j.asoc.2006.03.002
- Khan, M. S. and Coulibaly, P., 2006, Application of support vector machine in lake water level prediction, Journal of Hydrologic Engineering, 11(3), 199-205. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:3(199)
- Knotters, M. and Bierkens, M. F. P., 2000, Physical basis of time series models for water table depths, Water Resources Research, 36(1), 181-188. https://doi.org/10.1029/1999WR900288
- Nayak, P. C., Satyaji Rao, Y. R., and Sudheer, K. P., 2006, Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resources Management, 20, 77-90. https://doi.org/10.1007/s11269-006-4007-z
- Park, E. and Parker, J. C., 2008, A simple model for water table fluctuations in response to precipitation, Journal of Hydrology, 356, 344-349. https://doi.org/10.1016/j.jhydrol.2008.04.022
- Platt, J. C., 1999, Fast training of support vector machines using sequential minimal optimization. In:Scholkopf, B., Burges, C.J.C., Smolar, A.J. (Eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, Massachusetts, USA, 376p.
- Rai, S. N. and Singh, R. N., 1995, Two-dimensional modelling of water table fluctuation in response to localized transient recharge, Journal of Hydrology, 167, 167-174. https://doi.org/10.1016/0022-1694(94)02607-D
- Rumelhart, D. E., McClelland, J. L., and The PDP Research Group, 1986, Parallel Distributed Processing:Explorations in the Microstructure of Cognition. MIT Press, Cambridge, Massachusetts, USA, 516p.
- Sahoo, G. B., Ray, C., and De Carlo, E. H., 2006, Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii, Journal of Hydrology 327, 525-538. https://doi.org/10.1016/j.jhydrol.2005.11.059
- Suen, J. P. and Eheart, J. W., 2003, Evaluation of neural networks for modeling nitrate concentrations in rivers, Journal of Water Resources Planning and Management-ASCE 129(6), 505-510. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:6(505)
- Scholkopf, B. and Smola, A. J., 2002, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, Massachusetts, USA, 656p.
- Srivastava, K., Rai, S. N., and Singh, R. N., 2002, Modeling water-table fluctuations in a sloping aquifer with random hydraulic conductivity. Environmental Geology, 41(5), 520-524. https://doi.org/10.1007/s002540100385
- Tankersley, C. D., Graham, W. D., and Hatfield, K., 1993, Comparison of univariate and transfer function models of groundwater fluctuations. Water Resources Research, 29, 3517-3533. https://doi.org/10.1029/93WR01527
- van Geer, F. C. and Zuur, A. F., 1997, An extension of Box-Jenkins transfer/noise models for spatial interpolation of groundwater head series. Journal of Hydrology, 192, 65-80. https://doi.org/10.1016/S0022-1694(96)03113-7
- Vapnik, V. N., 1995, The Nature of Statistical Learning Theory, Springer-Verlag, New York, USA, 314p.
- Yi, M. J. and Lee, K. K., 2004, Transfer function-noise modeling of irregularly observed groundwater heads using precipitation data. Journal of Hydrology, 288, 272-287. https://doi.org/10.1016/j.jhydrol.2003.10.020
- Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., and Lee, K. K., 2011, A comparative study of artificial neural network and support vector machines for predicting groundwater levels in a coastal aquifer, Journal of Hydrology, 396, 128-138. https://doi.org/10.1016/j.jhydrol.2010.11.002
- Zealand, C. M., Burn, D. H., and Simonovic, S. P., 1999, Short-term streamflow forecasting using artificial neural networks, Journal of Hydrology, 214, 32-48. https://doi.org/10.1016/S0022-1694(98)00242-X
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