Sentence examples for relative root mean squared from inspiring English sources

With total 14 variables, the RF explains 73.25% of total variance in the dataset and the root mean squared error (RMSE) was 0.797 °C with the relative root mean squared error (rRMSE) was 4.98% (Fig. 4).

The metric error, the relative root mean squared error (rRMSE), is presented at different short time scales.

On the basis of the empirical values of relative bias and relative root mean squared error it was concluded that universal kriging and cokriging were more suitable in the presence of strong spatial autocorrelation of the forest variable, while locally weighted regression and k-nearest neighbors were more suitable when the auxiliary variables were well correlated with the response variable.

The comparison was performed on the basis of the actual relative root mean squared errors obtained from three artificial situations which mimic forest cover estimations.

The relative root mean square errors of the model for growth ranged between 0.09 and 0.31.

Simulation results indicate that the proposed methods perform competitively in terms of relative bias and relative root mean square error.

The correlation coefficient between the observed thicknesses in dataset 1 and the modelling results was 0.76 (Fig. 3e), with a relative root mean square error (RMSE) of 0.10.

The performance of all models was assessed two common statistical parameters: the relative root mean square error (RMSE) and the relative mean bias error (MBE).

The performance of all models was assessed with two common statistical parameters: the relative root mean square error (RMSE) and the relative mean bias error (MBE).

Also, the proposed model showed the lowest mean bias error (MBE) and relative root mean square error (RMSE) compared to the other models.

Validation of the models with the independent data indicated that all relative root mean square errors (RRMSEs) between the measured and simulated values were below 20%.