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Volume 10, issue 9 | Copyright
Geosci. Model Dev., 10, 3519-3545, 2017
https://doi.org/10.5194/gmd-10-3519-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

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 et al.
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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.
Accurate representation of land-atmosphere carbon fluxes is essential for future climate...
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