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Overall, root mean squared errors for runoff were reduced by 22.2% when hydrological model calibration is performed with WRF precipitation.
The models' R2 ranged from 0.71 to 0.88, and their root mean squared errors (RMSEs) ranged from 3.07 to 6.95.
Figure 7 shows the root mean squared errors (RMSE) of ETAM and LP-ETAM according to various.
Note that the mean prediction bias and root mean squared errors are based on leave-one dataset -out valeave-one dataset -out
The root mean squared errors (RMSEs) of parameter estimation are close to their Cramér-Rao lower bounds (CRLB).
In this section, Monte-Carlo tests are conducted to obtain the root mean squared errors (RMSEs) of the estimates.
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We compare the root mean-squared errors (RMSEs) of the proposed approach with MD/MM and WCMSR algorithms.
Optimized models had the root mean-squared errors of 26.1 nm/min and 0.103 nm for the etch rate and surface roughness, respectively.
Root mean-squared errors (RMSE), mean-absolute errors (MAE) and determination coefficient (R2) were used for the evaluation of the models' performances.
Female populations, open age interval 85+ Table 1 Root-mean squared errors (RMSEs) in life expectancy at birth e0, percentage RMSEs in life expectancy in the open age interval e a (by method): by sex, level of life expectancy at birth, and open age interval (a+) RMSE in e0 by method (years) Percentage RMSE in e a by method (percent) Sex e0 range a Classical Extrapol.
In assessing the root-mean-squared errors (RMSEs) of the different estimators, a Rayleigh fading channel is assumed.
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