OPTIMAL MEASUREMENT CONTROL STRATEGIES FOR NATURAL-RESOURCE SYSTEMS

The design of optimal polluting level measurement control to be used for the optimal estimation or prediction of the state of a stochastic environment system is considered. Filtering theory, an extension of Bayesian analysis to dynamic systems, is used to formulate a dynamic pollution level measurement optimization problem in which 'the state of the plant' is given by the elements of the variance-covariance matrix of pollution level estimates and the control is related to the determination of the locations of sampling sites and to finding the sequencing of measurements in time. The problem statement is in such a form so that the deterministic as well as the stochastic principle of Pontryagin can be applied to obtain the necessary conditions. The analysis shows that the formulation of the problem considered in this paper is sufficiently realistic and equally valid for the theory of the measurement control of any renewable or non-renewable resource system.