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EVALUATING PREDICTIVE ERRORS OF A COMPLEX ENVIRONMENTAL MODEL USING A GENERAL LINEAR MODEL AND LEAST SQUARE MEANS
KNIGHTES, C. D. AND M. J. CYTERSKI. EVALUATING PREDICTIVE ERRORS OF A COMPLEX ENVIRONMENTAL MODEL USING A GENERAL LINEAR MODEL AND LEAST SQUARE MEANS. ECOLOGICAL MODELLING. Elsevier Science BV, Amsterdam, Netherlands, 186(3):366-374, (2005).
The objective of this task is to develop, support and transfer a wide variety of tools and mathematical models that can be used to support watershed and water quality protection programs in support of OW, OSWER, and the Regions.
A General Linear Model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the Regional Mercury Cycling Model (R-MCM) to simulate epilimnetic total mercury concentrations in Vermont and New Hampshire lakes based on data gathered through the EPAs Regional Environmental Monitoring and Assessment Program (REMAP).
Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY
ECOSYSTEMS RESEARCH DIVISION
REGULATORY SUPPORT BRANCH