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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 2
Nonlin. Processes Geophys., 16, 313-317, 2009
https://doi.org/10.5194/npg-16-313-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 16, 313-317, 2009
https://doi.org/10.5194/npg-16-313-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  15 Apr 2009

15 Apr 2009

How much does inclusion of non-linearity and multi-point pattern recognition improve the spatial mapping of complex patterns of groundwater contamination?

M. Chowdhury1, A. Alouani1, and F. Hossain2 M. Chowdhury et al.
  • 1Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505-0001, USA
  • 2Department of Civil and Environmental Engineering, Tennessee Technological University, Cookeville, TN 38505-0001, USA

Abstract. In this brief communication, we discuss the implication of the hypothesis that "non-linearity and multi-point pattern recognition can improve the spatial mapping of complex patterns of groundwater contamination". The discussion is based on our recently published work in Stochastic Environmental Research and Risk Assessment. Therein we have found that the use of a highly non-linear pattern learning technique in the form of an artificial neural network (ANN) can yield significantly superior results under the same set of constraints when compared to the more linear two-point ordinary kriging method.

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