Evaluating the potential of Genetic Programming as an exploratory data analysis in soil science.

Abstract

Genetic Programming is a powerful optimization technique, able to deliver high-quality results in several real-world problems. One of its most successful applications is symbolic regression, where the objective is to find a suitable expression to model the underlying relationship between data points, with no aprioristic assumptions. In this paper, we propose the application of a Genetic Programming technique to a dataset on soil respiration and soil properties, in order to investigate possible influences of soil properties on soil respiration through symbolic regression. The best candidate models obtained by the technique are then studied to determine possible differences in the relationships related to environmental factors. Recurring patterns in the best solutions proposed by the search algorithm are identified, and the suitability of symbolic regression in soil science is evaluated and discussed. Genetic Programming proves to be an extremely promising data mining technique for soil scientists, as it is able to uncover relationships that could otherwise remain hidden, while remaining completely neutral and bias-free. We suggest its application for routine data analysis, as the technique presents particular interest for environmental modeling and development of pedotransfer functions.

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