Sentence examples for mean bias errors from inspiring English sources
The mean absolute errors (MAEs) and mean bias errors (MBEs) in the downscaled maximum daily temperature based on 30 weather stations reveal that the elevation-based downscaling approach is effective and the average errors do not significantly affect the spatial and temporal patterns of heating cycles in terms of the four key variables and frequency distributions of heating cycles.
Overall mean bias errors (MBE) below 9% and root mean square errors (RMSE) below 19% demonstrate that translucent materials can be modeled in Radiance with an even higher accuracy than was demonstrated in earlier validation studies for the plastic, metal, and glass material types.
Having Root Mean Square Errors (RMSE) in the range of 0.54 2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of −0.07 to 0.27 mm, respectively.
The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy.
Mean absolute error and mean bias error were adopted for accuracy assessment of the models.
The model efficiency is high, and the mean bias error and root mean bias error between the simulated and the observed values are minor.
These differences were also compared with the mean bias error and root-mean-squared error of the modelled GSR.
The average root mean square error, mean bias error, and absolute error at maximum power point of the proposed model are 1.713%, 0.149%, and 4.515%, respectively.
The accuracy of the each model was evaluated based on the correlation coefficient, mean absolute error, mean bias error and root mean square error values.
The most appropriate interpolation method was selected based on mean absolute error (MAE) and mean bias error (MBE) indices.
