Record Display for the EPA National Library Catalog

RECORD NUMBER: 12 OF 19

OLS Field Name OLS Field Data
Main Title Principles of Modelling.
Author Beck, M. B. ;
CORP Author Imperial Coll. of Science, Technology and Medicine, London (England). Dept. of Civil Engineering.;Environmental Research Lab., Athens, GA. Office of Research and Development.
Publisher c1991
Year Published 1991
Report Number EPA-R-816572; EPA/600/J-94/461;
Stock Number PB95-131116
Additional Subjects Water pollution ; Mathematical models ; Environment models ; Water quality ; Probability theory ; Calibrating ; Monte Carlo method ; Verifying ; Computerized simulation ; Error analysis ; Pollution control ; Experimental design. Reprints ;
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB95-131116 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 03/06/1995
Collation 10p
Abstract
The scope for modeling the behavior of pollutants in the aquatic environment is now immense. In many practical applications there are effectively no computational constraints on what is possible. There is accordingly an increasing need for a set of principles of modeling that in some respects may well be different from those applicable when conceptualization, the accuracy of the numerical solution scheme, and the inadequacies of an overly simplified model structure, were the issues of the day. Given the availability of increasingly comprehensive software, the user of a model is increasingly likely to be accelerated into a position where the issue of model calibration (identification) is an immediate problem. From the practical point of view of needing to make a decision on the control of a pollutant, the problem of identification may, or may not, be avoided. It is argued that a consistent approach to establishing whether such identification is necessary depends on establishing the significance, or otherwise, of model uncertainty. Identifying the model against field data does not have merely the goal of yielding best estimates of the unknown coefficients (parameters) appearing in the given model structure. (Copyright (c) 1991 IAWPRC.)