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We then tested the predictive capability of the models by predicting 30-day sorption/desorption data using kinetic parameters fitted on 1-day sorption/desorption data only.
Jackknife procedure was used to evaluate predictive capability of the models.
The quantitative and independent evaluation of the predictive capability of the models reveals that: (1) The most reliable susceptibility models are those derived from the nearest neighbour distance and sinkhole density.
The study further explores and evaluates the predictive capability of the models by various temporal scenarios, including 15-min ahead, 30-min ahead, 1-hour ahead, and 24-hour ahead forecasts.
We demonstrated and compared the predictive capability of the models and algorithms on respiratory 3D lung tumor motion time series for real-time prediction.
To prevent over fitting, the predictive capability of the models was checked by samples from the dataset, building a model and then predicting the dose for the omitted samples.
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Improvement in the predictive capability of the model is illustrated through comparison of the pressure gradient predicted by the proposed model with that of the existing models.
To demonstrate the excellent predictive capability of the model, several examples are analyzed numerically.
In addition, the predictive capability of the model equation was also found to be satisfactory.
The predictive capability of the model is also presented and discussed.
The predictive capability of the model was verified by comparing the theoretical calculations with the experimental results.
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