Sentence examples for model efficiency is from inspiring English sources
Essentially, the closer the model efficiency is to 1, the more accurate the model is.
The model efficiency is derived from the use of pre-computed solutions of the mass balance equations.
The model efficiency is high, and the mean bias error and root mean bias error between the simulated and the observed values are minor.
As a result, Nash-Sutcliffe model efficiency is increased from 0.52 to 0.81 for training and from 0.51 to 0.75 for forecasting.
As the NS model efficiency is biased to the higher values, the index of agreement (I A) is used and expressed as below (Legates and McCabe 1999): I_{A} = 1 - frac{{sumlimits_{i = 1}^{N} {(O_{i} - S_{i} )^{2} } }}{{sumlimits_{i = 1}^{N} {left( {left| {S_{i} - mu_{0} } right.| + left| {left. {O_{i} - mu_{0} } right|} right.} right)^{2} } }} (11 where μ 0 is the average observed water table depth.
Methods of dramatically improving joint model efficiency were highlighted.
The corresponding Nash Sutcliffe model efficiency was 0.95, which showed that the agreement between observations and computed results was satisfactory.
The model efficiency was estimated by RMSE (Root mean square error) and by RSE (Relative square error).
The authors compared forecasting results of individual networks with hybrid model and confirmed that hybrid model efficiency was better than individuals.
The model efficiency was lower for growth or smaller components such as foliage mass and root mass.
The model efficiency was compared to some published correlations such as Chew and Connally (1958), Beggs and Robinson (1975), Khan et al. (1987), and Naseri et al. (2005).
