Accurate measurements of Net Ecosystem Exchange of CO 2 between atmosphere and biosphere are required in order to estimate annual carbon budgets. These are typically obtained with Eddy Covariance techniques. Unfortunately, these techniques are often both noisy and incomplete, due to data loss through equipment failure and routine maintenance, and require gap-filling techniques in order to provide accurate annual budgets. In this study, a grammar-based version of Genetic Programming is employed to generate interpolating models for flux data. The evolved models are robust, and their symbolic nature provides further understanding of the environmental variables involved. © 2012 Springer-Verlag.
CITATION STYLE
Nicolau, M., Saunders, M., O’Neill, M., Osborne, B., & Brabazon, A. (2012). Evolving interpolating models of net ecosystem CO 2 exchange using grammatical evolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7244 LNCS, pp. 134–145). https://doi.org/10.1007/978-3-642-29139-5_12
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