Using Artificial Neural Network to Forecast Groundwater Depth in Union County Well
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.
[1] 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
[2] 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.
[3] Bierkens, M. F. P., (1998), "Modeling water table fluctuations by means
of a stochastic differential equation", Water Resources Research 34,
2485-2499.
[4] Knotters, M. and Bierkens, M. F. P., (2000), "Physical basis of time
series models forwater table depths", Water Resources Research 36(1),
181-188.
[5] 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.
[6] 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.
[7] 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
[8] 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.
[9] Rumelhart, D. E., Hinton, G. E., and Williams, R. J., (1986), Learning
representations by back propagating errors. Nature 323, 533-536.
[10] http://www.usgs.org/
[11] 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.
[12] Campolo, M., Andreussi, P., and Soldati, A., (1999), "River flood
forecasting with neural network model", Water Resources Research
35(4), 1191-1197.
[13] Thirumalaiah, K., and Deo, M. C., (2000), "Hydrological forecasting
using neural networks", Journal of Hydrologic Engineering 5(2), 180-
189.
[1] 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
[2] 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.
[3] Bierkens, M. F. P., (1998), "Modeling water table fluctuations by means
of a stochastic differential equation", Water Resources Research 34,
2485-2499.
[4] Knotters, M. and Bierkens, M. F. P., (2000), "Physical basis of time
series models forwater table depths", Water Resources Research 36(1),
181-188.
[5] 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.
[6] 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.
[7] 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
[8] 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.
[9] Rumelhart, D. E., Hinton, G. E., and Williams, R. J., (1986), Learning
representations by back propagating errors. Nature 323, 533-536.
[10] http://www.usgs.org/
[11] 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.
[12] Campolo, M., Andreussi, P., and Soldati, A., (1999), "River flood
forecasting with neural network model", Water Resources Research
35(4), 1191-1197.
[13] Thirumalaiah, K., and Deo, M. C., (2000), "Hydrological forecasting
using neural networks", Journal of Hydrologic Engineering 5(2), 180-
189.
@article{"International Journal of Architectural, Civil and Construction Sciences:57589", author = "Zahra Ghadampour and Gholamreza Rakhshandehroo", title = "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.", keywords = "Neural networks, groundwater depth, forecast.", volume = "4", number = "2", pages = "47-4", }