Commenced in January 2007
Paper Count: 31106
Using Artificial Neural Network to Forecast Groundwater Depth in Union County Well
Abstract:A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, trial and error method was used on groundwater depth time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S. different domains of 20, 40, 60, 80, 100, and 120 preceding day were examined and the 80 days was considered as effective length of the domain. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the groundwater depths were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed groundwater depths for all domains were determined. In general, groundwater depth forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted ground water depths utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that more accurate nature of measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m) in set #1. However, the size of input data in this set was 80 times the size of input data in set #2; a factor that may increase the computational effort unpredictably. It was concluded that 80 daily data may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334690Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2207
 P. D. Sreekanth, N. Geethanjali, P. D. Sreedevi, Shakeel Ahmed, N. Ravi Kumar and P. D. Kamala Jayanthi, (2009), "Forecasting groundwater level using artificial neural networks", CURRENT SCIENCE 96, 933-939
 Van Geer, F. C. and Zuur, A. F., (1997), ÔÇÿAn extension of Box-Jenkins transfer noise models for spatial interpolation of groundwater head series-, Journal of Hydrology 192, 65-80.
 Bierkens, M. F. P., (1998), "Modeling water table fluctuations by means of a stochastic differential equation", Water Resources Research 34, 2485-2499.
 Knotters, M. and Bierkens, M. F. P., (2000), "Physical basis of time series models forwater table depths", Water Resources Research 36(1), 181-188.
 Gail, M., Brion, T. R., Neelakantan and Lingireddy, S., (2002), "A neuralnetwork- based classification scheme for sorting sources and ages of fecal contamination in water", Water Res., 36, 3765-3774.
 Guan, P., Huang, D. and Zhou, B., (2004), "Forecasting model for the incidence of hepatitis A based on artificial neural network", World J. Gastroenterol., 10, 3579-3582.
 Morshed, J. and Kaluarachchi, J. J., (1998), "Parameter estimation using artificial neural network and genetic algorithm for free-product migration and recovery", Water Resources Research 34(5), 1101-1113
 Coulibaly, P., Anctil, F., Aravena, R., and Bobee, B., (2001), "Artificial neural network modeling of water table depth fluctuations", Water Resources Research 37(4), 885-896.
 Rumelhart, D. E., Hinton, G. E., and Williams, R. J., (1986), Learning representations by back propagating errors. Nature 323, 533-536.
 Maier, H. R. and Dandy, G. C., (2000), "Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and application", Environmental Modeling and Software, 15: 101-124.
 Campolo, M., Andreussi, P., and Soldati, A., (1999), "River flood forecasting with neural network model", Water Resources Research 35(4), 1191-1197.
 Thirumalaiah, K., and Deo, M. C., (2000), "Hydrological forecasting using neural networks", Journal of Hydrologic Engineering 5(2), 180- 189.