Confined Aquifer’s Hydraulic Parameters Estimation by a Generalized Regression Neural Network

Abstract

Precise estimation of aquifer hydraulic parameters can be an asset to more sustainable management of groundwater resources. The nonlinear correlation of aquifer parameters along with equation-level complicacy of aquifers’ charging functions can be efficiently handled using artificial intelligence models due to their flexibility in mapping between the observed data and output functions. This study focuses on performance comparisons for generalized regression neural network, artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) for estimation of hydraulic parameters, namely storage coefficient and transmissibility of confined aquifers. To acquire a greater precision of the estimated output functions, the principle component analysis of the pumping test data was initially undertaken to reduce the input dimensions by filtering out redundancies and insignificant variables’ correlations. Next, these data are passed into the training and validation process of the artificial intelligence models. Several error indices, mean absolute relative error (MARE), RMSE, MAE, RMRE, Bias and R2 were used during the validation of the prediction models. Finally, the estimated output functions were compared against the traditional and yet commonly used graphical Theis method. As an example, MARE in aquifer parameters estimation by ANN and graphical Theis method was 0.5564% and 1.1320%, respectively. More generally, among all the intelligence prediction models used to estimate the hydraulic parameters of a confined aquifer, ANFIS was more accurate and sensibly required much less computational time than the others and, hence, may be selected as the superior model in aquifer parameters estimation.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2:433–459

    Google Scholar 

  2. Abraham A (2005) Adaptation of fuzzy inference system using neural learning. In: Nedjah N, Macedo Mourelle L (eds) Fuzzy systems engineering. Springer, Berlin, pp 53–83

    Google Scholar 

  3. Azari T, Samani N, Mansoori E (2015) An artificial neural network model for the determination of leaky confined aquifer parameters: an accurate alternative to type curve matching methods. Iran J Sci Technol 39:463

    Google Scholar 

  4. Balkhair KS (2002) Aquifer parameters determination for large diameter wells using neural network approach. J Hydrol 265:118–128

    Google Scholar 

  5. Basheer I, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Google Scholar 

  6. Baum EB, Haussler D (1989) What size net gives valid generalization? Neural Comput 1:151–160

    Google Scholar 

  7. Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6:2812–2831

    Google Scholar 

  8. Carrera J, Neuman SP (1986) Estimation of aquifer parameters under transient and steady state conditions: 1. Maximum likelihood method incorporating prior information. Water Resour Res 22:199–210

    Google Scholar 

  9. Ch S, Mathur S (2012) Particle swarm optimization trained neural network for aquifer parameter estimation. KSCE J Civ Eng 16:298–307

    Google Scholar 

  10. Chang FJ, Chang Y-T (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10

    Google Scholar 

  11. Chen S, Cowan CF, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2:302–309

    Google Scholar 

  12. Cigizoglu HK (2003) Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrol Sci J 48:349–361

    Google Scholar 

  13. Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37:63–68

    Google Scholar 

  14. Coppola E Jr, Poulton M, Charles E, Dustman J, Szidarovszky F (2003) Application of artificial neural networks to complex groundwater management problems. Nat Resour Res 12:303–320

    Google Scholar 

  15. Dayhoff JE (1990) Neural network architectures: an introduction. Van Nostrand Reinhold Co, New York

    Google Scholar 

  16. Denai MA, Palis F, Zeghbib A (2004) ANFIS based modelling and control of non-linear systems: a tutorial. In: 2004 IEEE international conference on systems, man and cybernetics. IEEE, pp 3433–3438

  17. Dibike YB, Solomatine DP (2001) River flow forecasting using artificial neural networks. Phys Chem Earth B 26:1–7

    Google Scholar 

  18. Dowla FU, Rogers LL (1995) Solving problems in environmental engineering and geosciences with artificial neural networks. MIT Press, Cambridge

    Google Scholar 

  19. Firat M, Güngör M (2007) River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simul 75:87–96

    MathSciNet  MATH  Google Scholar 

  20. Gaur S, Ch S, Graillot D, Chahar BR, Kumar DN (2013) Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resour Manag 27:927–941

    Google Scholar 

  21. Ghomsheh VS, Shoorehdeli MA, Teshnehlab M (2007) Training ANFIS structure with modified PSO algorithm. In: 2007 MED’07 mediterranean conference on control and automation. IEEE, pp 1–6

  22. Goh A (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9:143–151

    Google Scholar 

  23. Gohari-Moghadam M, Samani N, Sleep B (2009) Aquifer parameter determination using a neuro-fuzzy approach. Paper presented at the international conference on water resources: emphasis on regional development. Shahrood University of Technology

  24. Hagan MT, Demuth HB, Beale MH, De Jesús O (1996) Neural network design. PWS publishing company, Boston

    Google Scholar 

  25. Haydari Z, Kavehnia F, Askari M, Ganbariyan M (2007) Time-series load modelling and load forecasting using neuro-fuzzy techniques. In: 2007 9th international conference on electrical power quality and utilisation. IEEE, pp 1–6

  26. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, New York

    Google Scholar 

  27. Jang JSR (1991) Fuzzy modeling using generalized neural networks and Kalman filter algorithm. AAAI, pp 762–767

  28. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Google Scholar 

  29. Jolliffe I (2002) Principal component analysis. Wiley Online Library, Hoboken

    Google Scholar 

  30. Karahan H, Ayvaz MT (2008) Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks. Hydrogeol J 16:817–827

    Google Scholar 

  31. Kumari N, Pathak G, Prakash O (2016) A review of application of artificial neural network in ground water modeling. In: Sinha AK, Rajesh R, Ranjan P, Singh RP (eds) Recent advances in mathematics, statistics and computer science. World Scientific Pub Co Inc., pp 393–402

  32. Li HZ, Guo S, Li CJ, Sun JQ (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl-Based Syst 37:378–387

    Google Scholar 

  33. Lin GF, Chen GR (2006) An improved neural network approach to the determination of aquifer parameters. J Hydrol 316:281–289

    Google Scholar 

  34. Lin HT, Tan YC, Chen CH, Yu HL, Wu SC, Ke KY (2010a) Estimation of effective hydrogeological parameters in heterogeneous and anisotropic aquifers. J Hydrol 389:57–68

    Google Scholar 

  35. Lin HT, Ke KY, Chen CH, Wu SC, Tan YC (2010b) Estimating anisotropic aquifer parameters by artificial neural networks. Hydrol Process 24:3237–3250

    Google Scholar 

  36. Maiti S, Tiwari R (2014) A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction. Environ Earth Sci 71:3147–3160

    Google Scholar 

  37. Negnevitsky M (2005) Artificial intelligence: a guide to intelligent systems. Pearson Education, London

    Google Scholar 

  38. Pan L, Wu L (1998) A hybrid global optimization method for inverse estimation of hydraulic parameters: annealing-simplex method. Water Resour Res 34:2261–2269

    Google Scholar 

  39. Polat K, Güneş S (2007) An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digit Signal Process 17:702–710

    Google Scholar 

  40. Rafiq M, Bugmann G, Easterbrook D (2001) Neural network design for engineering applications. Comput Struct 79:1541–1552

    Google Scholar 

  41. Rajesh M, Kashyap D, Hari Prasad K (2010) Estimation of unconfined aquifer parameters by genetic algorithms. Hydrol Sci J 55:403–413

    Google Scholar 

  42. Razavi S, Tolson BA (2011) A new formulation for feedforward neural networks. IEEE Trans Neural Netw 22:1588–1598

    Google Scholar 

  43. Sahin AU (2016) A new parameter estimation procedure for pumping test analysis using a radial basis function collocation method. Environ Earth Sci 75:1–13

    Google Scholar 

  44. Samani N, Gohari-Moghadam M, Safavi A (2007) A simple neural network model for the determination of aquifer parameters. J Hydrol 340:1–11

    Google Scholar 

  45. Samuel MP, Jha MK (2003) Estimation of aquifer parameters from pumping test data by genetic algorithm optimization technique. J Irrig drain Eng ASCE 129:348–359

    Google Scholar 

  46. Sawyer CS, Achenie LE, Lieuallen KK (1995) Estimation of aquifer hydraulic conductivities: a neural network approach. IAHS Publ Ser Proc Rep-Intern Assoc Hydrol Sci 227:177–184

    Google Scholar 

  47. Shlens J (2014) A tutorial on principal component analysis. arXiv preprint arXiv:14041100

  48. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC-15(1):116–132

    MATH  Google Scholar 

  49. Todd DK, Mays LW (2005) Groundwater hydrology. Wiley, Hoboken

    Google Scholar 

  50. Walton WC (1960) Leaky artesian aquifer conditions in Illinois. Report of investigation 39. State of Illinois, Department of Registration and Education, Illinois State Water Survey, Urbana

  51. Wang Y-M, Elhag TM (2008) An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Syst Appl 34:3099–3106

    Google Scholar 

  52. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2:37–52

    Google Scholar 

  53. Yun Z, Quan Z, Caixin S, Shaolan L, Yuming L, Yang S (2008) RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans Power Syst 23:853–858

    Google Scholar 

  54. Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6:2873–2888

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Gholamreza Rakhshandehroo.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest in this research.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Delnaz, A., Rakhshandehroo, G. & Nikoo, M.R. Confined Aquifer’s Hydraulic Parameters Estimation by a Generalized Regression Neural Network. Iran J Sci Technol Trans Civ Eng 44, 259–269 (2020). https://doi.org/10.1007/s40996-019-00238-2

Download citation

Keywords

  • Groundwater management
  • Aquifer parameter estimation
  • Artificial intelligence models
  • Artificial neural network
  • Generalized regression neural network