hdl:10013/epic.11053
Artificial neural network versus multiple linear regression:predicting P/B ratios from empirical data
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tbrey [ at ] awi-bremerhaven.de
Abstract
Traditionally, multiple linear regression models (MLR) are used to predict the somatic production/biomass (P/B) ratio of animal populations from empirical data of population parameters and environmental variables. Based on data from 899 benthic invertebrate populations, we compared the prediction of P/B by MLR models and by Artificial Neural Networks (ANN). The latter showed a slightly (about 6%) but significantly better performance. The accuracy of both approaches was low at the population level, but both MLR and ANN may be used to estimate production and productivity of larger population assemblages such as communities.
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Article
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Published
Eprint ID
463
DOI
https://www.doi.org/10.3354/meps140251
Cite as
Brey, T.
,
Jarre-Teichmann, A.
and
Borlich, O.
(1996):
Artificial neural network versus multiple linear regression:predicting P/B ratios from empirical data
,
Marine Ecology Progress Series,
140
(1-3),
pp. 251-256
.
doi: https://www.doi.org/10.3354/meps140251
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