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Modeling should thus focus on the physiological limits of species for maximum predictions.
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The predicted beam failure loads were compared with experimental results, with the obtained maximum prediction error of 8.4% indicating good agreement.
Receptivity is difficult to quantity and here we have used a space-time MBG framework to provide predictions of risk every five years from 1969 to 1989 and assumed the maximum prediction per 5 × 5 km grid to represent the maximal extent of predicted risk.
The maximum prediction uncertainties of FIA and Lefebvreʼs model are ±20%and±50%0%, respectively.
The maximum prediction error for surface hardness is less than 1.6%.
The predicted temperature values were in good agreement with the experimental data with the maximum prediction error of 6 °C.
Firstly, failure prediction models have been implemented through machine learning approaches using evaluated performance metrics and also demonstrates the maximum prediction accuracy for Naive Bayes.
The developed model was validated with a new set of experimental data, and the maximum prediction error of the model was less than 7%.
The LBO FAR obtained by FIA shows good agreement with experimental data, the maximum prediction uncertainty of FIA is about ±17.5%.
The results of the model have been compared with the experimental data of the pilot plant showing a maximum prediction error of 9%.
The simulated outlet temperatures of particulate foods had a good agreement with experimental data within the maximum prediction error of 4%.
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