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Model Input Parameters (model + input_parameter)
Selected AbstractsImproving extreme hydrologic events forecasting using a new criterion for artificial neural network selectionHYDROLOGICAL PROCESSES, Issue 8 2001Paulin Coulibaly Abstract The issue of selecting appropriate model input parameters is addressed using a peak and low flow criterion (PLC). The optimal artificial neural network (ANN) models selected using the PLC significantly outperform those identified with the classical root-mean-square error (RMSE) or the conventional Nash,Sutcliffe coefficient (NSC) statistics. The comparative forecast results indicate that the PLC can help to design an appropriate ANN model to improve extreme hydrologic events (peak and low flow) forecast accuracy. Copyright © 2001 John Wiley & Sons, Ltd. [source] Bioaccumulation Assessment Using Predictive Approaches,INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT, Issue 4 2009John W Nichols Abstract Mandated efforts to assess chemicals for their potential to bioaccumulate within the environment are increasingly moving into the realm of data inadequacy. Consequently, there is an increasing reliance on predictive tools to complete regulatory requirements in a timely and cost-effective manner. The kinetic processes of absorption, distribution, metabolism, and elimination (ADME) determine the extent to which chemicals accumulate in fish and other biota. Current mathematical models of bioaccumulation implicitly or explicitly consider these ADME processes, but there is a lack of data needed to specify critical model input parameters. This is particularly true for compounds that are metabolized, exhibit restricted diffusion across biological membranes, or do not partition simply to tissue lipid. Here we discuss the potential of in vitro test systems to provide needed data for bioaccumulation modeling efforts. Recent studies demonstrate the utility of these systems and provide a "proof of concept" for the prediction models. Computational methods that predict ADME processes from an evaluation of chemical structure are also described. Most regulatory agencies perform bioaccumulation assessments using a weight-of-evidence approach. A strategy is presented for incorporating predictive methods into this approach. To implement this strategy it is important to understand the "domain of applicability" of both in vitro and structure-based approaches, and the context in which they are applied. [source] Assessment of the Automobile Assembly Paint Process for Energy, Environmental, and Economic ImprovementJOURNAL OF INDUSTRIAL ECOLOGY, Issue 1-2 2004Geoffrey J. Roelant A coat of paint adds considerable value to an automobile. In addition to consuming up to 60% of the energy needed by automobile assembly plants, however, the painting process also creates both economic and environmental impacts. This study investigated the degree of cost and environmental impact improvement that can be expected when modifications are considered for existing paint processes through heat integration. In order to accomplish this goal, a mathematical model was created to describe the energy use, costs, and environmental impacts from energy consumption in an automobile assembly painting facility. The model agrees with measured energy consumption data for process heating and electricity demand to within about 15% for one Michigan truck facility from which model input parameters were obtained. Thermal pinch analysis determined an energy conservation target of 58% of paint process energy demand. A heat exchanger network optimization study was conducted in order to determine how closely the network design could achieve this target. The resulting heat exchanger network design was profitable based on a discounted cash flow analysis and may achieve reductions in total corporate energy consumption of up to 16% if implemented corporatewide at a major automobile manufacturer. [source] Bayesian calibration of computer modelsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2001Marc C. Kennedy We consider prediction and uncertainty analysis for systems which are approximated using complex mathematical models. Such models, implemented as computer codes, are often generic in the sense that by a suitable choice of some of the model's input parameters the code can be used to predict the behaviour of the system in a variety of specific applications. However, in any specific application the values of necessary parameters may be unknown. In this case, physical observations of the system in the specific context are used to learn about the unknown parameters. The process of fitting the model to the observed data by adjusting the parameters is known as calibration. Calibration is typically effected by ad hoc fitting, and after calibration the model is used, with the fitted input values, to predict the future behaviour of the system. We present a Bayesian calibration technique which improves on this traditional approach in two respects. First, the predictions allow for all sources of uncertainty, including the remaining uncertainty over the fitted parameters. Second, they attempt to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best-fitting parameter values. The method is illustrated by using data from a nuclear radiation release at Tomsk, and from a more complex simulated nuclear accident exercise. [source] | ||||||||||