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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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發(fā)表于 2025-3-21 19:50:47 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Uncertainty and Forecasting of Water Quality
編輯M. B. Beck,G. Straten,IIASA International Institut
視頻videohttp://file.papertrans.cn/942/941119/941119.mp4
圖書封面Titlebook: Uncertainty and Forecasting of Water Quality;  M. B. Beck,G. Straten,IIASA International Institut Book 1983 International Institute for App
描述Since the International Institute for Applied Systems Analysis began its study of water quality modeling and management in 1977, it has been interested in the relations between uncertainty and the problems of model calibration and prediction. The work has focused on the theme of modeling poorly defined environmental systems, a principal topic of the effort devoted to environmental quality control and management. Accounting for the effects of uncertainty was also of central concern to our two case studies of lake eutrophication management, one dealing with Lake Balaton in Hungary and the other with several Austrian lake systems. Thus, in November 1979 we held a meeting at Laxenburg to discuss recent method- ological developments in addressing problems associated with uncertainty and forecasting of water quality. This book is based on the proceedings of that meeting. The last few years have seen an increase in awareness of the issue of uncertainty in water quality and ecological modeling. This book is relevant not only to contemporary issues but also to those of the future. A lack of field data will not always be the dominant problem for water quality modeling and management; more so
出版日期Book 1983
關(guān)鍵詞Seen; Wassergüte; environment; quality control; simulation; uncertainty; water; water quality and water pol
版次1
doihttps://doi.org/10.1007/978-3-642-82054-0
isbn_softcover978-3-642-82056-4
isbn_ebook978-3-642-82054-0
copyrightInternational Institute for Applied Systems Analysis, Laxenburg/Austria 1983
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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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發(fā)表于 2025-3-22 00:45:16 | 只看該作者
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
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發(fā)表于 2025-3-22 19:16:38 | 只看該作者
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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