Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Ilie:2017:gmd,
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title = "Reverse engineering model structures for soil and
ecosystem respiration: the potential of gene expression
programming",
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author = "Iulia Ilie and Peter Dittrich and Nuno Carvalhais and
Martin Jung and Andreas Heinemeyer and
Micro Migliavacca and James I. L. Morison and
Sebastian Sippel and Jens-Arne Subke and Matthew Wilkinson and
Miguel D. Mahecha",
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journal = "Geoscientific Model Development",
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year = "2017",
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volume = "10",
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pages = "3519--3545",
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month = sep # "~25",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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ISSN = "1991-959X",
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bibsource = "OAI-PMH server at eprints.whiterose.ac.uk",
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format = "text",
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identifier = "Ilie, Iulia, Dittrich, Peter, Carvalhais, Nuno et al.
(8 more authors) (2017) Reverse engineering model
structures for soil and ecosystem respiration: the
potential of gene expression programming. Geoscientific
Model Development. gmd-2016-242. ISSN 1991-959X",
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oai = "oai:eprints.whiterose.ac.uk:120841",
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type = "PeerReviewed",
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URL = "http://eprints.whiterose.ac.uk/120841/1/GMD_Ilie_et_al_2016_finalAccepted.pdf",
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URL = "http://eprints.whiterose.ac.uk/120841/",
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DOI = "doi:10.5194/gmd-2016-242",
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size = "27 pages",
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abstract = "Accurate model representation of land-atmosphere
carbon fluxes is essential for climate projections.
However, the exact responses of carbon cycle processes
to climatic drivers often remain uncertain. Presently,
knowledge derived from experiments, complemented with a
steadily evolving body of mechanistic theory provides
the main basis for developing such models. The strongly
increasing availability of measurements may facilitate
new ways of identifying suitable model structures using
machine learning. Here, we explore the potential of
gene expression programming (GEP) to derive relevant
model formulations based solely on the signals present
in data by automatically applying various mathematical
transformations to potential predictors and repeatedly
evolving the resulting model structures. In contrast to
most other machine learning regression techniques, the
GEP approach generates readable models that allow for
prediction and possibly for interpretation. Our study
is based on two cases: artificially generated data and
real observations. Simulations based on artificial data
show that GEP is successful in identifying prescribed
functions with the prediction capacity of the models
comparable to four state-of-the-art machine learning
methods (Random Forests, Support Vector Machines,
Artificial Neural Networks, and Kernel Ridge
Regressions). Based on real observations we explore the
responses of the different components of terrestrial
respiration at an oak forest in south-east England. We
find that the GEP retrieved models are often better in
prediction than some established respiration models.
Based on their structures, we find previously
unconsidered exponential dependencies of respiration on
seasonal ecosystem carbon assimilation and water
dynamics. We noticed that the GEP models are only
partly portable across respiration components; the
identification of a general terrestrial respiration
model possibly prevented by equifinality issues.
Overall, GEP is a promising tool for uncovering new
model structures for terrestrial ecology in the data
rich era, complementing more traditional modelling
approaches.",
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notes = "also known as
\cite{oai:eprints.whiterose.ac.uk:120841}",
- }
Genetic Programming entries for
Iulia Ilie
Peter Dittrich
Nuno Carvalhais
Martin Jung
Andreas Heinemeyer
Micro Migliavacca
James I L Morison
Sebastian Sippel
Jens-Arne Subke
Matthew Wilkinson
Miguel D Mahecha
Citations