Groundwater Level Prediction Using SOM-RBFN Multisite Model
Publication: Journal of Hydrologic Engineering
Volume 15, Issue 8
Abstract
In this paper, a groundwater level forecasting model is proposed by combining the theory of self-organizing map (SOM) and radial basis function network (RBFN). The proposed model is referred to as SOM-RBFN model. Recently, RBFN has been applied in time series forecasting. Traditionally, the number of hidden units and the positioning of the radial basis centers are crucial problems for establishing RBFN. The proposed model can decide the number of RBFN’s hidden units with using the two-dimensional feature map which is constructed by SOM, and then it can determine the positioning of the radial basis centers easily. The proposed model is applied to actual groundwater level data in southern Taiwan from 1997 to 2003. It is found that the multisite model can predict the 1 month ahead groundwater level more precisely than the single-site model. Moreover, it is also found that the four-site model is more competent in predicting groundwater level as compared to the single-site model and six-site model. Therefore, too much information cannot improve the generalization ability of the model. For groundwater level prediction, the SOM-RBFN multisite model is recommended as an alternative to the other methods because it has a clear principle and a simple structure. In addition, it can produce more reasonable forecasts.
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References
Ahn, H., and Salas, J. D. (1997). “Groundwater head sampling based on stochastic analysis.” Water Resour. Res., 33(12), 2769–2780.
ASCE Task Committee. (1990a). “Review of geostatistics in geohydrology, I: Basic concepts.” J. Hydraul. Eng., 116(5), 612–632.
ASCE Task Committee. (1990b). “Review of geostatistics in geohydrology, II: Applications.” J. Hydraul. Eng., 116(5), 633–658.
ASCE Task Committee. (2000). “Artificial neural network in hydrology.” J. Hydrol. Eng., 5(2), 124–137.
Atiya, A. F. (2001). “Prediction for credit risk using neural networks: A survey and new results.” IEEE Trans. Neural Netw., 12(4), 929–935.
Box, G. E. P., and Jenkins, G. M. (1976). Time series analysis, forecasting and control, Holden-Day, San Francisco.
Bracq, P., and Delay, F. (1997). “Transmissivity and morphological features in a chalk aquifer: A geostatistical approach of their relation.” J. Hydrol., 191, 139–160.
Broomhead, D. S., and Lowe, D. (1988). “Multilvariable functional interpolation and adaptive networks.” Complex Systems, 2, 321–355.
Chang, F. J., and Chen, Y. C. (2003). “Estuary water-stage forecasting by using radial basis function neural network.” J. Hydrol., 270, 158–166.
Chen, S. K., Mangimeli, P., and West, D. (1995). “The comparative ability of self-organizing neural networks to define cluster structure.” Omega, 23(3), 271–279.
Chen, Z. H., Stephen, E. G., and Kirk, G. O. (2002). “Predicting average annual groundwater levels from climatic variables: An empirical model.” J. Hydrol., 260, 102–117.
Govindaraju, R. S., and Rao, A. R. (2000). Artificial neural networks in hydrology, Kluwer, Dordecht, The Netherlands.
Granger, C. W. J. (1993). “Strategies for modeling nonlinear time-series relationships.” Econ. Rec., 69(3), 233–238.
Gundogdu, K. S., and Guney, I. (2007). “Spatial analyses of groundwater levels using universal kriging.” J. Earth Syst. Sci., 116(1), 49–55.
Hamid, S. A., and Zahid, I. (2004). “Using neural network for forecasting volatility of S&P 500 index futures prices.” J. Bus. Res., 57, 1116–1125.
Haykin, S. (1994). Neural networks: A comprehensive foundation, IEEE, New York.
Hipel, K. W., and McLeod, A. I. (1994). Time series modeling of water resources and environmental systems, Elsevier, Amsterdam, The Netherlands.
Ho, S. L., Xie, M., and Goh, T. N. (2002). “A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction.” Comput. Ind. Eng., 42(2–4), 371–375.
Knotters, M., and Bierkens, M. F. P. (2000). “Physical basis of time series models for water table depths.” Water Resour. Res., 36, 181–188.
Knotters, M., and Van Walsum, V. (1997). “Estimation fluctuation quantities from time series of water-table depths using models with a stochastic component.” J. Hydrol., 197, 25–46.
Kohonen, T. (1990). “The self-organizing map.” Proc. Inst. Electr. Eng., 78(9), 1464–1480.
Kohonen, T. (1995). Self-organizing maps, Springer, Berlin.
Kohzadi, N., Boyd, M. S., Kermanshahi, B., and Kaastra, I. (1996). “A comparison of artificial neural network and time series models for forecasting commodity prices.” Neurocomputing, 10, 169–181.
Krishna, B., Satyaji Rao, Y. R., and Vijaya, T. (2008). “Modelling groundwater levels in an urban coastal aquifer using artificial neural networks.” Hydrolog. Process., 22(8), 1180–1188.
Kuan, C. M., and Liu, T. (1995). “Forecasting exchange rates using feedforward and recurrent neural networks.” J. Appl. Econ., 10, 347–364.
Kumar, D., and Ahmed, S. (2003). “Seasonal behaviour of spatial variability of groundwater level in a granitic aquifer in monsoon climate.” Curr. Sci., 84, 188–196.
Lin, G. F., and Chen, L. H. (2005). “Time series forecasting by combining the radial basis function network and the self-organizing map.” Hydrolog. Process., 19(10), 1925–1937.
Lin, G. F., and Chen, L. H. (2006). “Identification of homogeneous regions for regional frequency analysis using the self-organizing map.” J. Hydrol., 324(1–4), 1–9.
Mangiameli, P., Chen, S. K., and West, D. (1996). “A comparison of SOM neural network and hierarchical clustering methods.” Eur. J. Oper. Res., 93, 402–417.
Michaelides, S. C., Pattichis, C. S., and Kleovoulou, G. (2001). “Classification of rainfall variability by using artificial neural networks.” Int. J. Climatol., 21, 1401–1414.
Moody, J., and Darken, C. (1989). “Fast learning in networks of locally-tuned processing units.” Neural Comput., 4, 740–747.
Musavi, M. T., Ahmed, W., Chan, K. H., Faris, K. B., and Hummels, D. M. (1992). “On the training of radial basis function classifiers.” Neural Networks, 5(4), 595–603.
Nag, A. K., and Mitra, A. (2002). “Forecasting the daily foreign exchange rates using genetically optimized neural networks.” J. Forecast., 21, 501–511.
Orwig, R. E., Chen, H., and Nunamaker, J. F. (1997). “A graphical, self-organizing approach to classifying electronic meeting output.” J. Am. Soc. Inf. Sci., 48(2), 157–170.
Powell, M. J. D. (1987). “Radial basis functions for multivariable interpolation: A review.” Algorithms for approximation, J. C. Mason and M. G. Cox, eds., Clarendon, Oxford, U.K., 143–167.
Savic, D. A., Walters, G. A., and Davidson, J. W. (1999). “A genetic programming approach to rainfall-runoff modeling.” Water Resour. Manage., 13(3), 219–231.
Saxén, H. (1996). “Nonlinear time series analysis by neural networks: A case study.” Int. J. Neural Syst., 7(2), 195–201.
Schilling, R. J., Carroll, J. J., and Al-Ajlouni, A. F. (2001). “Approximation of nonlinear systems with radial basis function neural networks.” IEEE Trans. Neural Netw., 12(1), 1–15.
Shih, D. C. F., Lin, G. F., Jia, Y. P., Chen, Y. G., and Wu, Y. M. (2008). “Spectral decomposition of periodic groundwater fluctuation in a coastal aquifer.” Hydrolog. Process., 22(12), 1755–1765.
Tennant, W. T., and Hewitson, B. C. (2002). “Intra-seasonal rainfall characteristics and their importance to the seasonal prediction problem.” Int. J. Climatol., 22, 1033–1048.
Tokutaka, H., Yoshihara, K., Fujimura, K., Iwamoto, K., and Obu-Cann, K. (1999). “Application of self-organizing maps (SOM) to auger electron spectroscopy.” Surf. Interface Anal., 27, 783–788.
Vandycke, S., Bergerat, F., and Depuis, C. H. (1991). “Meso-cenozoic faulting and inferred palaeostresses in the Mons Basin, Belgium.” Tectonophysics, 192, 261–271.
van Geera, F. C., and Zuur, A. F. (1997). “An extension of Box-Jenkins transfer/noise models for spatial interpolation of groundwater head series.” J. Hydrol., 192, 65–80.
Wang, Z., Guerriero, A., and DeSario, M. (1996). “Comparison of several approaches for the segmentation of texture images.” Pattern Recogn. Lett., 17, 509–521.
Wasserman, P. D. (1993). Advanced methods in neural computing, Van Nostrand Reinhold, New York.
Zhang, G., Patuwo, B. E., and Hu, M. Y. (1998). “Forecasting with artificial neural networks: The state of the art.” Int. J. Forecast., 14, 35–62.
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Received: Apr 5, 2009
Accepted: Dec 11, 2009
Published online: Dec 15, 2009
Published in print: Aug 2010
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