Info

a With permission from Loague and Green (1991).

'Pi, predicted value i; Oi, observed value i; O, mean of observed values; n, number of data pairs.

single largest difference between a pair of predicted and observed values. The root mean square error represents the total difference between the predicted and the observed values, proportioned against the mean observed value. Modeling efficiency (EF) assesses the accuracy of simulations by comparing the variance of predicted from observed values to the variance of observed values from the mean of the observations. The EF is essentially a comparison of the efficiency of the chosen model to the efficiency of a very simple predictive model: the mean of the observations. The coefficient of determination is a measure of the proportion of the total variance in the observed data that is explained by the predicted data. Last, the coefficient of residual mass gives an indication of the consistent errors in the distribution of all simulated values across all measurements.

Ultimately, a model that performs well (i.e., in which modeled output matches observations reasonably well) is not necessarily a "good" model. A good model is conceptually clear and can be easily communicated to others. A bad model may perform well, but because it can be inspected and modified only with great difficulty, there is no way to determine if the validation is pure chance or something meaningful. It is important that the user understand the assumptions and limitations of a selected model at each stage of its use and application.

references and suggested reading

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