Abstract
A general definition of the predictive power (PP) of a model has been proposed and tested, where PP = R 2 /((1.1 - slope)-CV). R 2 is the mean r 2 -value of several validations, and (1.1 - slope) is the mean slope factor in such validations, i.e., regressions of independent empirical data versus model-predicted values. CV is the coefficient of variation for r 2 in these validations (regressions). Using this definition, it can be shown that: (a) within the range of application, empirical models (for radiocesium in lakes) seem to give higher PP than dynamic models; and (b) the highest PP is not necessarily given by the most complex dynamic model. This generally results from inclusion of model variables with a high variability and/or uncertainty. The predictive power of a model is governed by the weakness of its weakest part.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
