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Artificial Neural Network Model for Prediction of Groundwater Levels: Case Study

V. Venkatesan, P. Rajesh Prasanna

Abstract


There are many environmental concerns to the quantity of surface water and groundwater in the hydrological system. It is very important to estimate the groundwater levels by using readily available data for managing the water resources of the natural environment. As a case study in Sindappalli Uppodai Subbasin of Vaippar River basin in Tamilnadu an Artificial Neural Network(ANN) methodology was applied to estimate the groundwater levels as function of monthly precipitation, Evapotranspiration, lake water levels and roundwater level. Among the different robust tools available, the Back Propagation (BPNN) Artificial Neural Network model is commonly used to empirically forecast hydrological variables. The simulations results indicated that BP is accurate in reproducing (fitting) and forecasting the groundwater levels time series based on the R2 are 0.99 and 0.88, respectively. The RMSE, MAE for BP model in the predicting stage are 0.085, 0.076, respectively. It is evident that the BPNN is able to predict the groundwater levels reasonable well.


Keywords


Artificial Neural Networks, Back-Propagation, Groundwater levels, Forecasting

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References


Abrahart, R.J. & Kneale, P.E. Exploring neural network rainfall-runoff modelling. BHS National Hydrology Symposium, Salford, UK,9.35–9.43, 1997.

Aqil.M, Kita.I, A. Yano, and S. Nishiyama, “Analysis and prediction of flow from local source in a river basin suing a Neuro-fuzzy modeling tool,” J. Environ. Manage., vol. 85, pp. 215-223, October 2007.

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial Neural Networks in Hydrolgy. I Preliminery concepts. J.Hydol. Engg. 5(2) 115 – 123, 2000.

Basheer.I.A, and Hajmeer.M, “Artificial neural networks:fundamentals,computing, design, and application,” J. Micobiol. Methods., vol. 43, pp.3-31, December 2000.

Campolo, M., Soldati, A. & Andreussi, P. Artificial neural network approach to flood forecasting in the River Arno. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques 48:381–398, 2003.

Castellano-Méndez, W. González-Manteiga, M. Febrero-Bande, J. M. Prada-Sánchez, and R. Lózano-Calderon, “Modeling of the monthly and daily behavior of the runoff of the Xallas river using Box–Jenkins and neural networks methods,” J. Hydrol., vol. 296, pp. 38-58, August 2004.

Coulibaly.P, Anctil.F, Aravena.R, and Bobee.A, “Artificial neural network modeling of water table depth fluctuations,” Water Resour. Res.,Vol. 37, pp. 885-896, April 2001.

Daliakopoulos.I.N, Coulibaly.P, and I. K. Tsanis, “Groundwater level forecasting using artificial neural networks,” J. Hydrol., vol. 309, pp.229–240, July 2005.

Dell’Acqua, F. & Gamba, P.. Pyramidal rain field decomposition using radial basis function neural networks for tracking and forecasting purposes. IEEE Transactions on Geoscience and Remote Sensing 41:853–862, 2003.

French, M.N., Krajewski, W.F. & Cuykendall, R.R. Rainfall forecasting in space and time using a neural network. Journal of Hydrology 137:1–31,1992.

Fu.L, Neural networks in computer intelligence, McGraw Hill, New York. 1995.

Govindaraju.R.S & Rao.R (Eds.), Artificial Neural Networks in Hydrology, Dordrecht:Kluwer Academic, 2000.

Jiang.S.Y, Ren.Z.Y, Xue.K.M, and Li.C.F, “Application of BPANN for Prediction of backwark ball spinning of thin-walled tubular part with longitudinal inner ribs,” J. Mater. Process., vol. 196, pp. 190- 196, January 2008.

Kuo.Y.M, Liu.C.W, and Lin.K.H, “Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan,” Water Res., vol. 38, pp.148-158, January 2004.

Nayak.P.C, Sathyaji Rao.Y.R, Sudheer.K.P, “Groundwater level forecasting in a shallow aquifer using Artificail Neural Network approach”, Water resources management, Vol.20, pp. 77-90, 2006.

Rumelhart, D.E., Hinton, G.E. & Williams, R.J. Learning internal representations by error propagations. In D.E.Rumelhart & J.L.McClelland, (Eds.), Parallel Distributed Processing:Explorations in the Microstructures of Cognition 1 (pp. 318–362), 1986. Cambridge, MA: MIT Press.

Shepherd, A.J. Second-Order Methods for Neural Networks. London: Springer-Verlag, 1997.

Singh.V.P, and Woolhiser.D.A, “Mathematical modeling of watershed hydrology,” J. Hydrol. Eng., vol. 7, pp 270-292, July 2002.

Swingler.K, “Applying neural networks: a practical guide,” New York:Academic Press, 1996.

Wong.H, Ip.W.C, Zhang.R.Q, and Xia.J, “Non-parametric time series models for hydrological forecasting,” J. Hydrol., vol. 332, pp.337-347, January 2007.


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