Artificial Neural Network Approach for Predicting Transient Water Levels in a Multilayered Groundwater System under Variable State, Pumping, and Climate Conditions
Publication: Journal of Hydrologic Engineering
Volume 8, Issue 6
Abstract
The feasibility of training an artificial neural network (ANN) for accurately predicting transient water levels in a complex multilayered ground-water system under variable state, pumping, and climate conditions is demonstrated. Using real-world data, an ANN was developed for a public supply wellfield and ground-water monitoring network located near Tampa Bay, Florida. The ANN was trained to predict transient water levels at 12 monitoring well locations screened in different aquifers in response to changing pumping and climate conditions. The trained ANN was then validated with ten sequential seven-day periods, and the results were compared against both measured and numerically simulated ground-water levels. The absolute mean error between the ANN predicted and the measured water levels is 0.16 m, compared to the 0.85 m absolute mean error achieved with the calibrated numerical model at the same locations over the same time period. The ANN also more closely reproduced the dynamic water level responses to pumping and climate conditions. The practical implication is that if ANN technology can achieve superior ground-water level predictions, it can be used to improve management strategies for a wide range of ground-water problems, from water quantity to water quality issues. It can also serve as a powerful sensitivity analysis tool for quantifying interrelationships between different variables, fostering a better understanding of the hydrogeologic system, and improving future modeling endeavors. And while physical-based numerical modeling retains some advantages over the ANN technology, both approaches may be used in a complementary fashion to achieve sound decision-making for complicated ground-water management problems.
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References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology. I: Preliminary concept.” J. Hydraul. Eng., 5(2), 115–123.
ASCE Task Committee. (2000b). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydraul. Eng., 5(2), 124–137.
American Water Resources Association and Universities Council on Water Resources. (2001). Summer Specialty Conf., “Decision support systems for water resource management.” Preliminary program, June 27–30, Snowbird, Utah, American Water Resources Association.
Anderson, M., and Woessner, W. (1992). Applied groundwater modeling, Academic, San Diego, Calif.
Carrera, J., and Neuman, S. P.(1986a). “Estimation of aquifer parameters under transient and steady state conditions. Part 1: Maximum likelihood method incorporating prior information.” Water Resour. Res., 22(2), 199–210.
Carrera, J., and Neuman, S. P.(1986b). “Estimation of aquifer parameters under transient and steady state conditions. Part 2: Uniqueness, stability and solution algorithms.” Water Resour. Res., 22(2), 211–217.
Carrera, J., and Neuman, S. P.(1986c). “Estimation of aquifer parameters under transient and steady state conditions. Part 3: Application of synthetic and field data.” Water Resour. Res., 22(2), 228–242.
Clifton, P. M., and Neuman, S. P.(1982). “Effects of kriging and inverse modeling on conditional simulation of the Avra Valley aquifer in southern Arizona.” Water Resour. Res., 18(4), 1215–1234.
Coppola, E. (2000). “Optimal pumping policy for a public supply wellfield using computational neural network with decision-making methodology.” PhD thesis, Univ. of Arizona at Tucson, Ariz.
Coulibaly, P., Anctil, F., Aravena, R., and Bobee, B.(2001). “ANN modeling of water table depth fluctuations.” Water Resour. Res., 37(4), 885–896.
De Marisily, G. (1986). Quantitative hydrogeology, Academic, San Diego, Calif.
Gelhar, L.(1986). “Stochastic subsurface hydrology from theory to applications.” Water Resour. Res., 22(9), 135S–145S.
Gelhar, L. (1993). Stochastic subsurface hydrology, Prentice-Hall, Englewood Cliffs, N.J.
Harbaugh, A. W., Banta, E. R., Hill, M. C., and McDonald, M. G. (2000). “MODFLOW 2000, the U.S. geological survey modular ground-water model-user guide to modularization concepts and the ground-water flow process.” U.S. Geological Survey Open-File Rep. 00-92, Washington, D.C.
Hecht-Nielsen, R. (1987). “Counterpropagation networks.” Proc. of the Int. Conf. on Neural Networks, II, 19–31, IEEE Press, New York.
Konikow, L. F., and Bredehoeft, J. D.(1992). “Ground-water models cannot be validated.” Adv. Water Resour., 15, 75–83.
Mulhall, T.(1995). “How much data is needed to train a neural network.” Neural Edge, 8(Winter), 4–5.
Oreskes, N., Shrader-Frechette, K., and Belitz, K.(1994). “Verification, validation, and confirmation of numerical models in the earth sciences.” Science, 263, 641–646.
Oron, A. P., and Berkowitz, B.(1998). “Flow in rock fractures: The local cubic law assumption reexamined.” Water Resour. Res., 34(11), 2811–2825.
Poulton, M. (2001). Computational neural networks and neural networks for geophysical data processing, Pergamon, Amsterdam, The Netherlands.
Rogers, L. L., and Dowla, F. U.(1994). “Optimization of groundwater remediation using artificial neural networks with parallel solute transport modelling.” Water Resour. Res., 30(2), 457–481.
Ryder, P. D. (1985). “Hydrology of the Floridan aquifer system in west-central Florida.” U.S. Geological Survey Professional Paper 1403-F, Washington, D.C.
Sprecher, D.(1965). “On the structure of continuous functions of several variables.” Trans. Am. Math. Soc., 115, 340–355.
Sun, N.-Z., Jeng, M.-C., and Yeh, W. W.-G.(1995). “A proposed geological parameter identification in three-dimensional groundwater modeling.” Water Resour. Res., 31(1), 89–102.
Sun, N.-Z., and Yeh, W. W.-G.(1985). “Identification of parameter structure in groundwater inverse problem.” Water Resour. Res., 21(6), 869–883.
Swingler, K. (1996). Applying neural networks, Academic, San Diego, Calif.
Tompson, A. F. B., Ababou, R., and Gelhar, L. W.(1989). “Implementation of the three-dimensional turning bands random field generator.” Water Resour. Res., 25(10), 2227–2243.
Yeh, W. W.-G., and Yoon, Y. S.(1981). “Aquifer parameter identification with optimum decision in parameterization.” Water Resour. Res., 17(3), 664–672.
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Copyright © 2003 American Society of Civil Engineers.
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Received: Jul 2, 2002
Accepted: Apr 29, 2003
Published online: Oct 15, 2003
Published in print: Nov 2003
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