Predicting the Impact of Subsurface heterogeneous Hydraulic Conductivity on the Stochastic Behavior of Well Draw down in a Confined Aquifer Using Artificial Neural Networks

  • Abdin Alaa El-Din (National Water Research Center-Ministry of Water Resources and Irrigation, Egypt., Department of Engineering Math. & Physics, Faculty of Engineering-Cairo University, Egypt) ;
  • Abdeen Mostafa A. M. (National Water Research Center-Ministry of Water Resources and Irrigation, Egypt., Department of Engineering Math. & Physics, Faculty of Engineering-Cairo University, Egypt)
  • Published : 2005.08.01

Abstract

Groundwater flow and behavior have to be investigated based on heterogeneous subsurface formation since the homogeneity assumption of this formation is not valid. Over the past twenty years, stochastic approach and Monte Carlo technique have been utilized very efficiently to understand the groundwater flow behavior. However, these techniques require lots of computational and numerical efforts according to the various researchers' comments. Therefore, utilizing new techniques with much less computational efforts such as Artificial Neural Network (ANN) in the prediction of the stochastic behavior for the groundwater based on heterogeneous subsurface formation is highly appreciated. The current paper introduces the ANN technique to investigate and predict the stochastic behavior of a well draw down in a confined aquifer based on subsurface heterogeneous hydraulic conductivity. Several ANN models are developed in this research to predict the unsteady two dimensional well draw down and its stochastic characteristics in a confined aquifer. The results of this study showed that ANN method with less computational efforts was very efficiently capable of simulating and predicting the stochastic behavior of the well draw down resulted from the continuous constant pumping in the middle of a confined aquifer with subsurface heterogeneous hydraulic conductivity.

Keywords

References

  1. Abdeen, M. A. M., 2001, 'Neural Network Model for Predicting Flow Characteristics in Irregular Open Channels,' Scientific Journal, Faculty of Engineering-Alexandria University, Vol. 40, No.4, pp. 539-546
  2. Abdin, A. E. and Abdeen, M. A. M., 1999, 'Effect of Subsurface Heterogeneity on the Unsteady Well Draw Down in a Confined Aquifer,' ASCE Conference, Washington-USA
  3. Chan, T. P. and Govindaraju, R. S., 2003 'A New Model for Soil Hydraulic Properties Based on a Stochastic Conceptualization of Porous Media,' Water Resour. Res, Vol. 39, No.7 https://doi.org/10.1029/2002WR001954
  4. Chandramouli, V. and Raman, H., 2001, 'Multireservoir Modeling With Dynamic Programming and Neural Networks,' Journal of Water Resources Planning and Management, Vol. 127, pp.89-98 https://doi.org/10.1061/(ASCE)0733-9496(2001)127:2(89)
  5. Chang, C. M., Kemblowski, Mw. W., Kaluarachchi, J. J. and Abdin, A. E., 1995a, Stochastic Analysis of two-phase Flow in Porous media: I. Spectral/Perturbation Approach, Transport in Porous Media, 19: pp.233-259 https://doi.org/10.1007/BF00617531
  6. Chang, C. M., Kemblowski, Mw. W. Kaluarachchi, J. J. and Abdin, A. E., 1995b, Stochastic Analysis of Multiphase Flow in Porous Media: I. Spectral/Perturbation Approach, Stochastic Hydrology and Hydraulics, 9: pp.239-267 https://doi.org/10.1007/BF01581722
  7. Gebremariam, A. A., 2002, 'Determining Aquifer Parameters Using Pumping Test Data,' PhD Thesis, Ethiopia, September
  8. Indelman, P., Dagan, G., Cheng, A. H. -D. and Ouazar, D., 1996, 'Sensitivity Analysis of Flow in Multilayered Leaky Aquifer,' Journal of Hydraulic Engineering, Vol. 122, No.1, pp. 41-45 https://doi.org/10.1061/(ASCE)0733-9429(1996)122:1(41)
  9. Kheireldin, K. A., 1998, 'Neural Network Application for Modeling Hydraulic Characteristics of Severe Contraction,' Proceeding of the Third International Conference, Hydroinformatics, Copenhagen-Denmark, August 24-26
  10. Kuo-lin Hsu, Hoshin V. Gupta, Xiaogang Gao, Sorooshian and Bisher Imam, 2002, 'Self-organizing Linear Output map (SOLO) : An Artificial Neural Network Suitable for Hydrologic Modeling and Analysis,' Water Resour. Res., Vol. 38, No. 12 https://doi.org/10.1029/2001WR000795
  11. Mantoglou, A. and Wilson, J. L., 1982, The Turning Bands Method for Simulation of Random Fields Using Line Generation by a Spectral Method, Water Resour. Res., 18 (5), pp. 1379-1394 https://doi.org/10.1029/WR018i005p01379
  12. Minns, 1996, 'Extended Rainfall-Runoff Modeling Using Artificial Neural Networks,' Proceeding of the Second International Conference on Hydroinformatics, Zurich, Switzerland
  13. Oliver, D. S., 1998, 'The Influence of Nonuniform Transmissivity and Storativity on Draw Down,' Water Resour. Res., Vol. 29, No.1, pp.169-178 https://doi.org/10.1029/92WR02061
  14. Park, Ji-Hyung and Kwang-Kyu Seo, 2003, 'Approximate Life Cycle Assessment of Product Concepts Using Multiple Regression Analysis and Artificial Neural Networks, KSME International Journal, Vol. 17, No. 12
  15. Ramanitharan, K. and Li, C., 1996, 'Forecasting Ocean Waves Using Neural Networks,' Proceeding of the Second International Conference on Hydroinformatics, Zurich, Switzerland
  16. Shin, Y., 1994, 'NeuralystTM User's Guide,' Neural Network Technology for Microsoft Excel, Cheshire Engineering Corporation Publisher
  17. Solomatine, D. and Toorres, L., 1996, 'Neural Network Approximation of a Hydrodynamic Model in Optimizing Reservoir Operation,' Proceeding of the Second International Conference on Hydroinformatics, Zurich, Switzerland
  18. Tahk, Kyung-Mo and Kee-Hyun Shin, 2002, 'A Study on the Fault Diagnosis of Roller-Shape Using Frequency Analysis of Tension Signals and Artificial Neural Networks Based Approach in a Web Transport System,' KSME International Journal, Vol. 16, No. 12
  19. Tawfik, M., Ibrahim, A. and Fahmy, H., 1997, 'Hysteresis Sensitive Neural Network for Modeling Rating Curves,' ASCE, Journal of Computing in Civil Engineering, Vol. 11, No.3
  20. Yeh, T. -C. J., Gelhar, L. W. and Gutjahr, A. L., 1985a, Stochastic Analysis of Unsaturated Flow in Heterogeneous Soils, 1, Statistically Isotropic Media, Water Resour. Res., 21 (4), pp.447-456 https://doi.org/10.1029/WR021i004p00447