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A random search methodology for examining parametric uncertainty in water quality models
O O Osidele1, W Zeng, M B Beck
1Warnell School of Forest Resources, University of Georgia, Athens, GA 30602, USA. fosidele@uga.edu
Summary
Modern computers enable efficient Monte Carlo methods for environmental models. A new method, uniform covering by probabilistic rejection (UCPR), enhances random searching for optimal parameters and uncertainty analysis.
Area of Science:
- Environmental modeling
- Computational methods
Background:
- Monte Carlo methods are increasingly useful for complex environmental simulations.
- Computational burden previously limited random searching techniques.
Purpose of the Study:
- To introduce and evaluate the Uniform Covering by Probabilistic Rejection (UCPR) method.
- To enhance the efficiency of random searching in parameter identification.
Main Methods:
- UCPR combines pure random search with probabilistic rejection.
- Nearest-neighbor distances guide an ensemble of points to optimal parameter vectors.
- Applied to a dynamic river water quality model for sediment-transport nutrient dynamics.
Main Results:
- UCPR significantly enhances the efficiency of random search algorithms.
- Identified a complex, interactive parameter structure in the Oconee River model.
- Multiple optimal parameter sets were found across a wide feasible domain.
Conclusions:
- UCPR provides a realistic depiction of parameter uncertainty.
- The method is effective for identifying parameters in complex environmental models.
- Results highlight the intricate relationships within riverine nutrient dynamics.
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