Skip to main content
Log in

Comparison of Groundwater Level Estimation Using Neuro-fuzzy and Ordinary Kriging

  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

Water level in aquifer plays the main role in groundwater modeling as one of the input data. In practice, due to aspects of time and cost, data monitoring of water levels is conducted at a limited number of sites, and interpolation technique such as kriging is widely used for estimation of this variable in unsampled sites. In this study, the efficiency of the ordinary kriging (OK) and adaptive network-based fuzzy inference system (ANFIS) was investigated in interpolation of groundwater level in an unconfined aquifer in the north of Iran. The results showed that ANFIS model is more efficient in estimating the groundwater level than OK.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Cameron, K., & Hunter, P. (2002). Using spatial models and kriging techniques to optimize long-term ground-water monitoring networks: A case study. Environmetrics, 13, 629–656. doi:10.1002/env.582.

    Article  Google Scholar 

  2. Chang, L. -C., & Chang, F. -J. (2001). Intelligent control for modeling of real-time reservoir operation. Hydrological Processes, 15(9), 1621–1634. doi:10.1002/hyp.226.

    Article  Google Scholar 

  3. Chen, S. H., Lin, Y. H., Chang, L. C., & Chang, F. J. (2006). The strategy of building a flood forecast model by neuro-fuzzy network. Hydrological Processes, 20(7), 1525–1540. doi:10.1002/hyp.5942.

    Article  Google Scholar 

  4. Cheng, C. B., & Lee, E. S. (1999). Applying fuzzy adaptive network to fuzzy regression analysis. Computers & Mathematics with Applications (Oxford, England: 1987), 38, 123–140. doi:10.1016/S0898-1221(99)00187-X.

    Article  Google Scholar 

  5. Davis, J. C. (2002). Statistics and data analysis in geology (2nd ed.). New York: Wiley 1986.

    Google Scholar 

  6. Desbarats, A. J., Logan, C. E., Hinton, M. J., & Sharpe, D. R. (2002). On the kriging of groundwater table elevations using collateral information form a digital elevation model. Geological Survey of Canada, 255, 25–38.

    Google Scholar 

  7. Diamond, P. (1988). Interval valued random functions and the kriging of intervals. Mathematical Geology, 20, 145–165. doi:10.1007/BF00890251.

    Article  Google Scholar 

  8. Hasebe, M., & Nagayama, Y. (2002). Reservoir operation using the neural network and fuzzy systems for dam control and operation support. Advances in Engineering Software, 33, 245–260. doi:10.1016/S0965-9978(02)00015-7.

    Article  Google Scholar 

  9. Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics. New York: Oxford University Press.

    Google Scholar 

  10. Jang, J. -S. R. (1993). ANFIS: adaptive network based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665–684. doi:10.1109/21.256541.

    Article  Google Scholar 

  11. Jang, C. S., & Liu, C. W. (2004). Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui river alluvial fan. Taiwan. Hydrological Processes, 18, 1333–1350. doi:10.1002/hyp.1397.

    Article  Google Scholar 

  12. Lee, E. S. (2000). Neuro-fuzzy estimation in spatial statistics. Journal of Mathematical Analysis and Applications, 249, 221–231. doi:10.1006/jmaa.2000.6938.

    Article  Google Scholar 

  13. Ma, T. S., Sophocleous, M., & Yu, Y. S. (1999). Geostatistical applications in ground-water modeling in south–central Kansas. Journal of Hydrologic Engineering, 4(1), 57–64. doi:10.1061/(ASCE)1084-0699(1999)4:1(57).

    Article  Google Scholar 

  14. Marsily, G. D., & Ahmed, S. (1987). Application of kriging techniques in groundwater hydrology. Journal of the Geological Society of India, 29(1), 57–79.

    Google Scholar 

  15. Mukhopadhyay, A. (1999). Spatial estimation of transmissivity using artificial neural network. Ground Water, 37(3), 458–464. doi:10.1111/j.1745-6584.1999.tb01125.x.

    Article  CAS  Google Scholar 

  16. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. Journal of Hydrology (Amsterdam), 10, 282–290. doi:10.1016/0022-1694(70)90255-6.

    Article  Google Scholar 

  17. Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology (Amsterdam), 291, 52–66. doi:10.1016/j.jhydrol.2003.12.010.

    Article  Google Scholar 

  18. Piotrowski, J. A., Bartels, F., Salski, A., & Schmidt, G. (1996). Geostatistical regionalization of glacial aquitard thickness in northwestern Germany, based on fuzzy kriging. Mathematical Geology, 28(4), 437–448. doi:10.1007/BF02083655.

    Article  Google Scholar 

  19. Ponnambalam, K., Karray, F., & Mousavi, S. J. (2003). Minimizing variance of reservoir systems operations benefits using soft computing tools. Fuzzy Sets and Systems, 139, 451–461. doi:10.1016/S0165-0114(02)00546-8.

    Article  Google Scholar 

  20. Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386–408. doi:10.1037/h0042519.

    Article  CAS  Google Scholar 

  21. Sepaskhah, A. R., Ahmadi, S. H., & Nikhbakht Shahbazi, A. R. (2004). Geostatistical analysis of sorptivity for a soil under tilled and no-tilled conditions. Soil & Tillage Research, 86(2), 237–245.

    Google Scholar 

  22. Setnes, M., Babuska, R., & Verbruggen, H. B. (1998). Transparent fuzzy modeling. International Journal of Human–Computer Studies, 49, 159–179. doi:10.1006/ijhc.1998.0197.

    Article  Google Scholar 

  23. Takagi, T., & Suegeno, M. (1985). Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, 116–132.

    Google Scholar 

  24. Tutmez, B., Hatipoglu, Z., & Kaymak, U. (2006). Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Computers & Geosciences, 32, 421–433. doi:10.1016/j.cageo.2005.07.003.

    Article  CAS  Google Scholar 

  25. Wang, Y., Ma, T., & Luo, Z. (2001). Geostatistical and geochemical analysis of surface water leakage into groundwater on a regional scale: a case study in the Liulin karst system, northwest China. Journal of Hydrology (Amsterdam), 246, 223–234. doi:10.1016/S0022-1694(01)00376-6.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. M. Hosseini.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kholghi, M., Hosseini, S.M. Comparison of Groundwater Level Estimation Using Neuro-fuzzy and Ordinary Kriging. Environ Model Assess 14, 729–737 (2009). https://doi.org/10.1007/s10666-008-9174-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-008-9174-2

Keywords

Navigation