Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3519-2017
https://doi.org/10.5194/gmd-10-3519-2017
Development and technical paper
 | 
25 Sep 2017
Development and technical paper |  | 25 Sep 2017

Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha

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Cited articles

Ashworth, J., Wurtmann, E. J., and Baliga, N. S.: Reverse engineering systems models of regulation: Discovery, prediction and mechanisms, Curr. Opin. Biotechnol., 23, 598–603, https://doi.org/10.1016/j.copbio.2011.12.005, 2012.
Auger, A. and Hansen, N.: A restart CMA evolution strategy with increasing population size, 2005 IEEE Congress on Evolutionary Computation, 2, 1769–1776, https://doi.org/10.1109/CEC.2005.1554902, 2005.
Bandt, C. and Pompe, B.: Permutation entropy: a natural complexity measure for time series, Phys. Rev. Lett., 88, 174102, https://doi.org/10.1103/PhysRevLett.88.174102, 2002.
Bennett, N. D., Croke, B. F., Jakeman, A. J., Newham, L. T. H., and Norton, J. P.: Performance evaluation of environmental models, in: 2010 International Congress on Environmental Modelling and Software Modelling for Environment's Sake, 1–9, http://scholarsarchive.byu.edu/iemssconference/2010/all/247/ (last access: September 2017), 2010.
Beyer, H.-G. and Schwefel, H.-P.: Evolution Strategies, Natrual Computing, 1, 3–52, 2002.
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Short summary
Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
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