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Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence.
Results reveal that radar and multisensor QPEs lead to improved hydrologic model performance compared to simulations driven with rain gauge data only with respect to the observed streamflow.
An empirical evaluation of a probabilistic user model including self-explanation found that gaze-tracking data improved model performance compared to using only time data as a predictor of self-explanation.
The result showed improvement of model performance: compared with PLS model, the root mean square errors of prediction (RMSEP) of iPLS model decreased from 0.111 mg to 0.068 mg, and the R2 parameter increased from 0.9434 to 0.9801.
Although the most complex model reproduced the field observations best, the marginal improvement in model performance compared to simpler models was outweighed by the higher costs of increased complexity.
However, based on a parity of PBIAS, NSE and IoA values, hourly scale tile drainage computed using the modified model equipped with transient equations did not improve model performance compared with the original ssH equation.
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During the first testing period the ANN model (Model B3) shows a better performance compared to Model C3 and Model D3.
In contrast, features which cover only 48 h were found to construct models with consistent performance compared to the model using data from the entire sampling period (Fig. 2b).
Extensive simulation results demonstrate that our models exhibit superior performance compared to other network types suggested in the literature.
The proposed model shows an improved performance compared with existing models reported in the literature.
The experimental results show that the distinct model has better training performance compared to the homogeneous model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com