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Optimization algorithms based on wrapper models show better results but the processes involved in them are time intensive.
It can be observed that in most cases the Forward models show better relation with the lithologs than those of the Inversion model.
The results of the ANFIS models show better agreement with the experimental results as compared to the results of previous methods available in the literature.
It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters.
The results show that, all models give a good prediction of user performance, even if the cognitive models show better accuracy of the user performance.
The results indicate that the statistical and hybrid models show better rainfall predictions than NMME-EM and NMME-BMA models, which have good predictability only in southern China.
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Statistically, mid-IR spectral models showed better performance for 60% of the studied properties.
The FE models showed better predictions since more sophisticated modeling techniques and material constitutive models were used.
The ILPM-CFD model is validated over experimental and CFD data and is compared with LPM Constant Efficiency models showing better performance and a wider range of applicability.
Modeling of the breakthrough point (As concentration ⩽ 0.01 mg L−1) data by the Thomas and the Adams Bohert models showed better fit of data with Thomas model (R2 = 0.98 0.99) than Adams Bohert model (R2 = 0.90 0.94).
In the first part of the paper, the integrated lumped parameter-Computational Fluid Dynamics approach has been formulated, validated and compared with different constant efficiency models, showing better performance and a wider range of applicability.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com