The advent of the modern high-speed digital computer has tremendously enhanced the utility of Monte Carlo methods for evaluating complex environmental simulation models. In particular, random searching is becoming popular, as thousands of model runs can now be executed quickly and with minimal effort. Indeed, the issues of computational burden and inefficiency, hitherto the bane of random searching, are now receding. This paper presents one such method, uniform covering by probabilistic rejection (UCPR), which combines a pure random search with a probabilistic rejection algorithm that significantly enhances its efficiency. Using nearest-neighbor distances, an ensemble of points in a predefined parameter sampling domain migrates to locate and define a final distribution of optimal parameter vectors, thus providing a realistic depiction of parameter uncertainty. In a prototypical case study of the Oconee River (Georgia, USA), UCPR and regionalized sensitivity analysis, are employed for identifying the parameters of sediment-transport-associated nutrient dynamics, a dynamic river water quality model. Results indicate the existence of a complex interactive parameter structure, evidenced by multiple sets of optimal points widely dispersed over a broad domain of feasible parameter values.
A random search methodology for examining parametric uncertainty in water quality models
O.O. Osidele, W. Zeng, M.B. Beck; A random search methodology for examining parametric uncertainty in water quality models. Water Sci Technol 1 January 2006; 53 (1): 33–40. doi: https://doi.org/10.2166/wst.2006.005
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