Sentence examples for model efficiency criterion from inspiring English sources

The performance of the VPMM method is evaluated using the Nash Sutcliffe model efficiency criterion as the objective function to be maximized using the ROPE algorithm.

The Nash Sutcliffe model efficiency criterion, percentage error in volume, the percentage error in peak, and net difference of observed and simulated time to peak which were used for performance evaluation, have been found to range from 74.2 to 95.1%, 2.9 to 20.9%, 0.1 to 20.8% and −1 to 3 h, respectively, indicating a good performance of the GGIUH model for prediction of runoff hydrograph.

The objectives of this research were to compare the thermal inactivation kinetics of human norovirus surrogates (murine norovirus (MNV-1), and feline calicivirus (FCV-F9)) and HAV in buffered medium (2-ml vials), compare first-order and Weibull models to describe the data, calculate Arrhenius activation energy for each model, and evaluate model efficiency using selected statistical criteria.

The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness.

Two statistical criteria were examined: adjusted model efficiency (ME adj ) and root mean square error (RMSE).

The calibration was based on the Kling-Gupta Efficiency (KGE) criterion, while model transferability was evaluated based on the Nash-Sutcliffe efficiency criterion and volume error.

The performance of hydrological models can be assessed for instance by calculating the Nash Sutcliffe efficiency criterion (ENS).

The capability to make precise predictions for each model was evaluated with statistical error criteria, coefficient of determination (R 2), Nash Sutcliffe model efficiency coefficient (E), and RMSE.

The proposed SimHyd rainfall runoff model performs well for the simulation of natural monthly runoff over 1954 1969, with Nash and Sutcliffe efficiency criterion (NSE) of 82.6 % and the relative error of volumetric fit (RE) of 0.32 %.

Where, R efficiency criterion represents the percentage of the initial uncertainty explained by the model.

The error criteria are estimated using coefficient of determination (R 2), Nash Sutcliffe model efficiency coefficient (E), and RMSE.