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

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.