Examination of model uncertainty and parameter interaction
- Univ. of California, Berkeley, CA (United States). Environmental Engineering and Health Sciences Lab.
Complex simulation models are used to predict the consequences of releasing chemicals to the environment, and there appears to be a trend toward the acceptance of modeling results in the regulatory decision-making process. For example, groundwater transport models are being used to develop soil cleanup levels at Superfund sites, and multimedia exposure models are being used in the development of draft soil screening levels for 30 common Superfund soil contaminants. There are, however, substantial risks associated with regulatory decisions that are guided by the predictions derived from these models. There is a need to establish the magnitude and sources of uncertainty associated with model predictions in order to achieve a better understanding of simulated systems, increase the reliability of model predictions, guide field surveys and laboratory experiments, and define realistic values that should be used in subsequent risk assessments. Monte Carlo simulation is a basic tool that is now used routinely to quantify uncertainties associated with model predictions. However, the results of Monte Carlo analyses provided little information about the interaction between parameters. A new tree-structured density estimation technique has been developed that extends the ability of Monte Carlo-based analyses to explore parameter uncertainty and interaction in complex environmental models. The application of the technique is demonstrated using the multimedia model MMSOILS. The results consist of the evaluation of model performance and an examination of the effects of uncertainty on risk assessments at several hazardous waste facilities.
- OSTI ID:
- 49477
- Report Number(s):
- CONF-9410273--
- Country of Publication:
- United States
- Language:
- English
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