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The performance of proposed models is compared to the existing physical modeling and statistical modeling based techniques.
Then one of the numerical models is compared to measurements.
The effectiveness of coupled models is compared with traditional single models.
The behavior of the scrambled randomized models is compared in terms of their sampling errors.
The performance of the optimization models is compared with the methods used in practice and literature.
The empirical behavior of parsimonious additive models is compared to the adaptive backfitting BRUTO algorithm.
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Models were compared using Akaike information criterion (AIC).
A total of eight candidate models were compared using the package MuMIn in R (v 1.15.6 57.
The performances of different models were compared.
The models were compared using the Deviance Information Criterion.
The two numerical models were compared.
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