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A comparison of three-dimensional interpolation techniques for plume characterization.

Norman L Jones1, R Jeffrey Davis, Walid Sabbah

  • 1Environmental Modeling Research Laboratory, 242 Clyde Building, Brigham Young University, Provo, UT 84602, USA. njones@et.byu.edu

Ground Water
|July 23, 2003
PubMed
Summary

Kriging and inverse distance weighted interpolation are effective for contaminant plume characterization. Kriging showed the lowest error at most sites, while inverse distance weighted performed well overall.

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Area of Science:

  • Environmental Science
  • Geostatistics
  • Data Analysis

Background:

  • Interpolating contaminant data is challenging due to clustered samples and sharp concentration gradients.
  • Accurate three-dimensional plume characterization is crucial for environmental risk assessment.

Purpose of the Study:

  • To evaluate and compare the accuracy of common interpolation schemes for three-dimensional contaminant plume characterization.
  • To assess the impact of interpolation parameters on accuracy.

Main Methods:

  • Applied Kriging, natural neighbor, and inverse distance weighted interpolation to four real-world contaminant data sets.
  • Utilized the cross-validation approach to gauge the accuracy of each interpolation scheme.
  • Analyzed the effect of various interpolation parameters on scheme performance.

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Main Results:

  • Kriging yielded the lowest interpolation error at three out of four sites.
  • Inverse distance weighted interpolation demonstrated good overall performance and lower error at one site.
  • Natural neighbor interpolation exhibited the highest average error across all sites.
  • High-order nodal functions unexpectedly reduced accuracy for inverse distance weighted and natural neighbor methods.

Conclusions:

  • Kriging and inverse distance weighted interpolation are reliable methods for contaminant plume mapping.
  • The choice of interpolation scheme impacts accuracy, with Kriging often being superior.
  • Further research is needed to understand the performance of advanced methods with clustered data.