Titlebook: Uncertainty and Forecasting of Water Quality; M. B. Beck,G. Straten,IIASA International Institut Book 1983 International Institute for App

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The Validity and Credibility of Models for Badly Defined Systemsnd incontestable. Similarly, a model would be deemed credible if it was deserving of or entitled to belief, or if it was plausible, tenable, or reasonable. All of these characteristics are, of course, desirable in a mathematical model of a physical system; but when used as the basis for the definiti

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An Approach to the Analysis of Behavior and Sensitivity in Environmental Systemsor example, Berlinski, 1976) and in terms of the sophistication of representations of the “realities of the natural world” (Hedgepeth, 1977). Regardless of one’s philosophy concerning construction of “ecosystem” models, we argue that certain elements of such criticism must be addressed. In dealing w

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Input Data Uncertainty and Parameter Sensitivity in a Lake Hydrodynamic Modelr quality problems. The one- and two-dimensional model versions most frequently employed have two major parameters, the wind drag coefficient and the bottom friction coefficient. Although a number of important experiments have been performed in relation to the drag coefficient (for example Wu, 1969;

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Maximum Likelihood Estimation of Parameters and Uncertainty in Phytoplankton Modelsnts. Moreover, most parameters are lumped parameters in the sense that they represent a wealth of underlying processes for which separate modeling is undesirable or impractical, so that their numerical value has a well-defined physical meaning only for the system under study within the context of th

propose

Analysis of Prediction Uncertainty: Monte Carlo Simulation and Nonlinear Least-Squares Estimation ofire water body is thermally stratified from April to December and completely mixed after the late-autumn overturn (the lake is monomictic). Given the rather small lake area and its simple morphometry, the water body is considered horizontally homogeneous and a one-dimensional submodel was developed

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Multidimensional Scaling Approach to Clustering Multivariate Data for Water-Quality Modelingring, control, and managing problems of water pollution and eutrophication to obtain a comprehensive view of water quality in their own areas. Because of the variety of parameters observed as water-quality data, and the complexity and uncertainty involved in pollution and eutrophication mechanisms i

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Nonlinear Steady-State Modeling of River Quality by a Revised Group Method of Data Handlingoxygen production; therefore the structure of any physical model that considers the influence of these phenomena is necessarily very complex (Rinaldi et al., 1976, 1979). Parameter estimation procedures for physical models (Rinaldi et al., 1976) that have been used for predicting pollution levels of

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