Estimation of Ground Water Parameters by Developing a New Fuzzy Optimized Simulation Model

  • Nima EiniEmail author
  • Fidan Aslanova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1095)


Proper groundwater management is considered as one of the main sources of accurate groundwater parameters. Groundwater parameters are usually ignored in order to simplify the equations and uncertainty of seepage testing. In this research, an optimized simulation model has been developed to consider uncertainties in determining groundwater parameters. The optimized simulation model can accurately estimate the parameters of ground water due to the minimization of the difference between observation reduction and computational reduction loss. The proposed method is compared to the actual seepage test in the enclosed groundwater and its results are compared with the Thies method. Statistical error based on the results of proposed modeling simulation and the Thies method is solved by comparing the performance of two methods. For example, the average absolute relative error of the suggested simulation model and the Thies curve solution is respectively 0.69 and 1.13%, which indicates the accuracy of the proposed simulation model to the Thies solution. In the second section, due to the amount of discharge as an uncertain parameter, an optimized fuzzy simulated model is developed based on the fuzzy transformation method. Based on the results of this fuzzy method, the effect of this uncertainty on the optimal estimation of enclosed groundwater parameters has been investigated and the variation of groundwater parameters has been determined in different fuzzy sections. The results of two developed fuzzy methods show that the effect of discharge uncertainty on the estimation of the groundwater T parameter is higher than S.


Groundwater parameters Fuzzy transform method Genetic algorithm Seepage phenomenon 


  1. 1.
    Lingireddy, S.: Aquifer parameter estimation using genetic algorithms and neural networks. Civil Eng. Syst. 15, 125–144 (1998)CrossRefGoogle Scholar
  2. 2.
    Samuel, M.P., Jha, M.K.: Estimation of aquifer parameters from pumping test data by a genetic algorithm optimization technique. J. Irrig. Drainage Eng. 129, 348–359 (2003)CrossRefGoogle Scholar
  3. 3.
    Kerachian, R., Fallahnia, M., Bazargan-Lari, M.R., Mansoori, A., Sedghi, H.: A fuzzy game-theoretic approach for groundwater resources management. Application of Rubinstein bargaining theory. Resour. Conserv. Recycl. 54, 673–682 (2010)CrossRefGoogle Scholar
  4. 4.
    Abdel-Gawad, H.A.A.A., Elhadi, H.A.: Parameter estimation of pumping test data using a genetic algorithm. In: Thirteenth International Water Technology Conference, IWTC 13 (2009)Google Scholar
  5. 5.
    Rajesh, M., Kashyap, D., Hari Prasad, K.: Estimation of unconfined aquifer parameters by genetic algorithms. Hydrol. Sci. J. 55, 403–413 (2010)CrossRefGoogle Scholar
  6. 6.
    Lu, C., Shu, L., Chen, X., Cheng, C.: Parameter estimation for a karst aquifer with unknown thickness using the genetic algorithm method. Environ. Earth Sci. 63(4), 797–807 (2011)CrossRefGoogle Scholar
  7. 7.
    Bakhteni, S., Mortazavi-Naeini, M., Ataie-Ashtiani, B., Jeng, D., Khanbilvardi, R.: Evaluation of methods for estimating aquifer hydraulic parameters. Appl. Soft Comput. 28, 541–549 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhuang, C., Zhou, Z., Zhan, H., Wang, G.: A new type curve method for estimating aquitard hydraulic parameters in a multi-layered aquifer system. J. Hydrol. 527, 212–220 (2015)CrossRefGoogle Scholar
  9. 9.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefGoogle Scholar
  10. 10.
    Hanss, M.: The transformation method for the simulation and analysis of systems with uncertain parameters. Fuzzy Sets Syst. 130, 277–289 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hanss, M., Willner, K.: A fuzzy arithmetical approach to the solution of finite element problems with uncertain parameters. Mech. Res. Commun. 27, 257–272 (2000)CrossRefGoogle Scholar
  12. 12.
    Aramaki, T., Matsuo, T.: Evaluation model of policy scenarios for basin-wide water resources and quality management in the Tone River Japan. Water Sci. Technol. 38, 59–67 (1998)CrossRefGoogle Scholar
  13. 13.
    Sadegh, M., Kerachian, R.: Water resources allocation using solution concepts of fuzzy cooperative games: fuzzy least core and fuzzy weak least core. Water Resour. Manag. 25, 2543–2573 (2011)CrossRefGoogle Scholar
  14. 14.
    Nikoo, M.R., Kerachian, R., Karimi, A., Azadnia, A.A.: Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study. Environ. Monit. Assess. 185, 2483–2502 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Civil Engineering, Faculty of Civil and Environmental EngineeringNear East UniversityNicosiaTurkey
  2. 2.Department of Environmental Engineering, Faculty of Civil and Environmental EngineeringNear East UniversityNicosiaTurkey

Personalised recommendations