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The table below summarises some difficulties in model evaluation.
|The appropriate evaluation method depends on the context of the application and the data sets available||An array of various evaluation methods must be developed.||What weight should be ascribed to various performance measures?|
|Input data sets are limited. They reflect only few of the possible scenarios.||a) Extrapolate model behaviour outside of validation domain|| a) Does the model give the right result for the right reason? |
We must understand model behaviour!
|b) Use many data sets|| b) Hard work! |
|Processing of input data for model evaluation is far from trivial.|| Take care! |
Use quality indicators.
|The luxury of independent data sets can rarely be afforded.||Use many data sets.|| Hard work! |
|There are inherent uncertainties.||Use Venkatram's conceptual framework.||Ensembles are difficult to establish.|
This question was asked on the wiki: Atmospheric Dispersion Modelling.