Stochastic response surface methods (SRSMs) for uncertainty propagation: Application to environmental and biological systems

Journal Article · Mon Jun 01 04:00:00 EDT 1998 · Risk Analysis

DOI:https://doi.org/10.1111/j.1539-6924.1998.tb01301.x· OSTI ID:642333

Isukapalli, S S; Roy, A; Georgopoulos, P G ^[1]

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1. Rutgers Univ., Piscataway, NJ (United States). Environmental and Occupational Health Sciences Inst.

Comprehensive uncertainty analyses of complex models of environmental and biological systems are essential but often not feasible due to the computational resources they require. Traditional methods, such as standard Monte Carlo and Latin Hypercube Sampling, for propagating uncertainty and developing probability densities of model outputs, may in fact require performing a prohibitive number of model simulations. An alternative is offered, for a wide range of problems, by the computationally efficient Stochastic Response Surface Methods (SRSMs) for uncertainty propagation. These methods extend the classical response surface methodology to systems with stochastic inputs and outputs. This is accomplished by approximating both inputs and outputs of the uncertain system through stochastic series of well behaved standard random variables; the series expansions of the outputs contain unknown coefficients which are calculated by a method that uses the results of a limited number of model simulations. Two case studies are presented here involving (a) a physiologically-based pharmacokinetic (PBPK) model for perchloroethylene (PERC) for humans, and (b) an atmospheric photochemical model, the Reactive Plume Model (RPM-IV). The results obtained agree closely with those of traditional Monte Carlo and Latin Hypercube Sampling methods, while significantly reducing the required number of model simulations.

• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: FC01-95EW55084
• OSTI ID: 642333
• Journal Information: Risk Analysis, Journal Name: Risk Analysis Journal Issue: 3 Vol. 18; ISSN 0272-4332; ISSN RIANDF
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
56 BIOLOGY AND MEDICINE
APPLIED STUDIES
AIR POLLUTION
CHLORINATED ALIPHATIC HYDROCARBONS
DATA COVARIANCES
HEALTH HAZARDS
P CODES
PHOTOCHEMICAL REACTIONS
PLUMES
R CODES
S CODES