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FuzzyKrig: a comprehensive matlab toolbox for geostatistical estimation of imprecise information

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Abstract

Using kriging has been accepted today as the most common method of estimating spatial data in such different fields as the geosciences. To be able to apply kriging methods, it is necessary that the data and variogram model parameters be precise. To utilize the imprecise (fuzzy) data and parameters, use is made of fuzzy kriging methods. Although it has been 30 years since different fuzzy kriging algorithms were proposed, its use has not become as common as other kriging methods (ordinary, simple, log, universal, etc.); lack of a comprehensive software that can perform, based on different fuzzy kriging algorithms, the related calculations in a 3D space can be the main reason. This paper describes an open-source software toolbox (developed in Matlab) for running different algorithms proposed for fuzzy kriging. It also presents, besides a short presentation of the fuzzy kriging method and introduction of the functions provided by the FuzzyKrig toolbox, 3 cases of the software application under the conditions where: 1) data are hard and variogram model parameters are fuzzy, 2) data are fuzzy and variogram model parameters are hard, and 3) both data and variogram model parameters are fuzzy.

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References

  • Aalianvari A, Katibeh H, Sharifzadeh M (2010) A new approach for computing permeability of fault zones case study: the upper reservoir of Azad pumped-storage power station in Iran. Arch Min Sci 55(3):605–621

    Google Scholar 

  • Aelion CM et al (2009) Validation of Bayesian kriging of arsenic, chromium, lead, and mercury surface soil concentrations based on internode sampling. Environ Sci Technol 43(12):4432–4438

    Article  Google Scholar 

  • Bardossy A, Bogardi I, Kelly WE (1988) Imprecise (fuzzy) information in geostatistics. Math Geol 20(4):287–311

    Article  Google Scholar 

  • Bardossy A, Bogardi I, Kelly WE (1990a) Kriging with imprecise (fuzzy) variograms. I: theory. Math Geol 22(1):63–79

    Article  Google Scholar 

  • Bardossy A, Bogardi I, Kelly WE (1990b) Kriging with imprecise (fuzzy) variograms. II: application. Math Geol 22(1):81–94

    Article  Google Scholar 

  • Bartels F (1997) Ein Fuzzy-Auswertungs-und Krigingsystem für raumbezogene Daten, in Informatik und Praktische Mathematik. Universität Kiel

  • Cressie NAC (1993) Statistics for spatial data. John Wiley & Sons, New York

  • Davis B (1987) Uses and abuses of cross-validation in geostatistics. Math Geol 19(3):241–248

    Article  Google Scholar 

  • Diamond P (1989) Fuzzy kriging. Fuzzy Sets Syst 33(3):315–332

    Article  Google Scholar 

  • Diamond P, Armstrong M (1984) Robustness of variograms and conditioning of kriging matrices. J Int Assoc Math Geol 16(8):809–822

    Article  Google Scholar 

  • Falivene O et al (2010) Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example. Comput Geosci 36(4):512–519

    Article  Google Scholar 

  • Fletcher R (1987) Practical methods of optimization (2nd ed). John Wiley & Sons, New York

  • Ghaderi M, Hezarkhani A, Talebi M (2007) The use of litho-geochemical data and fluid inclusions in the study of Iju porphyry copper deposit, Northwest of Shahr-e-Babak, AmirKabir. J Sci Technol 67(3):51–63

    Google Scholar 

  • Handcock MS, Stein ML (1993) A Bayesian analysis of kriging. Technometrics 35(4):403–410

    Article  Google Scholar 

  • Hock W, Schittkowski K (1983) A comparative performance evaluation of 27 nonlinear programming codes. Computing 30(4):335–358

    Article  Google Scholar 

  • Loquin K, Dubois D (2010) Kriging and epistemic uncertainty: a critical discussion. In: Jeansoulin R et al (eds) Methods for handling imperfect spatial information. Springer, Berlin Heidelberg, pp 269–305

    Chapter  Google Scholar 

  • Loquin K, Dubois D (2012) A fuzzy interval analysis approach to kriging with ill-known variogram and data. Soft Comput 16(5):769–784

    Article  Google Scholar 

  • Nelles O (2001) Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, Berlin; New York

    Book  Google Scholar 

  • Piotrowski JA et al (1996) Geostatistical regionalization of glacial aquitard thickness in northwestern Germany, based on fuzzy kriging. Math Geol 28(4):437–452

    Article  Google Scholar 

  • Powell MJD (1978) A fast algorithm for nonlinearly constrained optimization calculations, in Numerical Analysis. Springer, Berlin Heidelberg, pp 144–157

    Google Scholar 

  • Schelin L, Luna S (2010) Kriging prediction intervals based on semiparametric bootstrap. Math Geosci 42(8):985–1000

    Article  Google Scholar 

  • Soltani-Mohammadi S, Tercan E (2012) Constrained multiple indicator kriging using sequential quadratic programming. Comput Geosci 48:211–219

    Article  Google Scholar 

  • Taboada J et al (2008) Evaluation of the reserve of a granite deposit by fuzzy kriging. Eng Geol 99(1-2):23–30

    Article  Google Scholar 

  • Webster R, Oliver MA (2007) Geostatistics for Environmental Scientists. John Wiley & Sons, New York

Download references

Acknowledgments

The author is indebted to Professor H. A. Babaie and the anonymous reviewers for their valuable comments on an earlier draft of this paper.

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Correspondence to Saeed Soltani-Mohammadi.

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Communicated by: H. A. Babaie

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Soltani-Mohammadi, S. FuzzyKrig: a comprehensive matlab toolbox for geostatistical estimation of imprecise information. Earth Sci Inform 9, 235–245 (2016). https://doi.org/10.1007/s12145-015-0240-4

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  • DOI: https://doi.org/10.1007/s12145-015-0240-4

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