Thereafter, the POD model reduced the dimensions of simulated nitrate in the computational grids.
The inclusion of an acidification term in the cutting time prediction model reduced the SEP to 7.9 min.
For the PD control responses, the non-linear model reduced the mean absolute prediction error by a factor of three.
Incorporating spatial structure via the FHCAR model reduced the CV by 56.9% and 10.8% relative to the direct and the FH model estimates, respectively.
The proposed δu model reduced the average of errors (A) and standard deviation (SD) by 15.1%, 3.9%, respectively, compared to the best predictions by previous models.
An inversion method based on the known PSF model reduced the signals originating from surrounding areas by as much as 53%.
Adding a spatial autocovariate term to the LR model (autologistic model) reduced the spatial autocorrelation and improved model performance, but did not correct the misidentification of the dominant environmental determinant.
The best model reduced the error up to 79% in terms of mean absolute error compared with error in water balance equation when conventional (with no correction coefficients) water balance analysis was conducted.
In addition, selection of such a homogeneous model reduced the simulation run time without compromising the results.
The factory model reduced the per-student cost of education sufficiently so that wealthy countries could, for the first time ever, provide free and compulsory K-12 education to all children.
Applying the predictive model reduced the number of crossmatches by 30% without underproviding RBC units and increased the percentage of patients crossmatched exactly for the required number of units from 11% to 21%.
