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Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

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Abstract

Aquifer porosity indicates the storage groundwater capacity and groundwater quality. It may be measured via different techniques. This paper presents a novel spatial methodology based on radial basis function (RBF) and neuro-fuzzy inference system for modelling the porosity. Use of the point cumulative semimadogram in RBF as a spatial measure is a novel contribution. In addition, the methodology examines the use of a neural network-based fuzzy inference system for porosity estimation. Performance comparisons with conventional methods show that the proposed spatial model has high modelling and generalization capability.

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References

  1. Tutmez B, Hatipoglu Z (2007) Spatial estimation model of porosity. Comput Geosci 33:465–475

    Article  Google Scholar 

  2. Taud H, Martinez-Angeles R, Parrot JF, Hemandez-Escobedo L (2005) Porosity estimation method by X-ray computed tomography. J Petrol Sci Eng 47:209–217

    Article  Google Scholar 

  3. Clausnitzer V, Hopmans JW (1999) Determination of phase-volume fractions from tomographic measurements in two-phase systems. Adv Water Resour 22(6):577–584

    Article  Google Scholar 

  4. Bardossy GY, Fodor J (2004) Evaluation of uncertainties and risks in geology. Springer, Heidelberg

    MATH  Google Scholar 

  5. Demicco RV, Klir GJ (2004) Fuzzy logic in geology. Elsevier, Amsterdam

    Google Scholar 

  6. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice-Hall, London

    Google Scholar 

  7. Dixon B (2005) Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. J Hydrol 309(1–4):17–38

    Article  Google Scholar 

  8. Tutmez B, Hatipoglu Z, Kaymak U (2006) Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Comput Geosci 32:421–433

    Article  Google Scholar 

  9. Powel MJD (1987) Radial basis functions for multivariable interpolation: a review. In: Mason JC, Cox MG (eds) Algorithms for approximation. Oxford University Press, New-York

    Google Scholar 

  10. Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355

    MATH  MathSciNet  Google Scholar 

  11. Lin GF, Chen LH (2004) A spatial interpolation method based on radial basis function networks incorporating a semivariogram model. J Hydrol 288:288–298

    Article  Google Scholar 

  12. Deutsch CV, Journel AG (1998) Geostatistical software library (GSLIB) and user’s guide. Oxford University Press, New-York

    Google Scholar 

  13. Tutmez B, Tercan AE, Kaymak U (2007) Fuzzy modelling for reserve estimation based on spatial variability. Math Geol 39(1):86–111

    Article  MathSciNet  Google Scholar 

  14. Leski J, Czogala E (1999) A new artificial neural network based fuzzy inference systems with moving consequents in if-then rules and its applications. Fuzzy Sets Syst 108:289–297

    Article  MATH  MathSciNet  Google Scholar 

  15. Czogala E, Leski J (2000) Fuzzy and neuro-fuzzy intelligent systems. Physica-Verlag, Heidelberg

    MATH  Google Scholar 

  16. Xie XL, Beni GA (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841–847

    Article  Google Scholar 

  17. Luger GF, Stubblefield WA (1998) Artificial intelligence: structure and strategies for complex problem solving. Addison Vesley Longman, London

    Google Scholar 

  18. Leski J, Czogala E (1997) A new artificial neural network based fuzzy inference systems with moving consequents in if-then rules. BUSEFAL 71:72–81

    Google Scholar 

  19. Piegat A (2001) Fuzzy modeling and control. Physica-Verlag, New York

    MATH  Google Scholar 

  20. Hatipoglu Z (2004) Hydrogeochemistry of Mersin-Tarsus coastal aquifer, Ph.D Thesis, Hacettepe University, Ankara

  21. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203

    Google Scholar 

  22. Webster R, Oliver MA (2001) Geostatistics for environmental scientists. Wiley, Chichester

    MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Engineering, Inonu University, 44280, Malatya, Turkey

    Bulent Tutmez

Authors
  1. Bulent Tutmez
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Correspondence to Bulent Tutmez.

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Tutmez, B. Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system. Neural Comput & Applic 19, 499–505 (2010). https://doi.org/10.1007/s00521-009-0326-3

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  • DOI: https://doi.org/10.1007/s00521-009-0326-3

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