Combining deterministic and statistical models for ill-defined systems: Advantages for air quality assessment

A.J. Jakeman, R.W. Simpson and J.A. Taylor

Mathematics and Computers in Simulation (MATCOM), 1985, vol. 27, issue 2, 167-178

Abstract: Uncertainty pervades the description of ill-defined systems and the data collected from the for model development. Young (1) has used a systems theoretic framework to espouse a general theory of modeling based upon the scientific method to cope with uncertainty. We show a hybrid deterministic/statistical approach consistent with this general theory can be used when such systems have a phenomenological property which can be simply characterised. The methodology is especially relevant to the assessment of air quality systems and details are provided of a comprehensive program within the Centre for Resource and Environmental studies (CRES) to develop a suite of algorithms for predicting the probability distribution of ambient pollutant concentrations from a range of emission sources.

Date: 1985
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:27:y:1985:i:2:p:167-178

DOI: 10.1016/0378-4754(85)90037-0

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