Modeling Environmental Systems under Uncertainty: Towards a Synthesis of Data-based and Theory-based Models by
نویسندگان
چکیده
A two-pronged modeling approach, in which both data-based modeling and theory-based modeling methods are jointly incorporated, has been developed. Its purpose is to gain a deeper understanding of complex, poorly-defined environmental systems. The Data-BasedMechanistic methodology is employed in the data-based modeling procedure for structure identification and parameter estimation of a data-based model (DBM, in transfer function forms). In the theory-based modeling prong, the Recursive Prediction Error algorithm is modified and engaged in estimating time-varying parameters and detecting structural change for a theory-based model (TBM, in ordinary differential forms). Two concepts from linear processes in control system engineering, time constant and steady-state gain, are then examined in a synthesis of the two types of model in the parameter space spanned by these two lumped parameters. Case studies on two environmental systems, an activated sludge system and an aquaculture pond, have been carried out to test the effectiveness of this proposed modeling approach. The results of both case studies have shown that: (1) more is gained through the joint application of the two separate modeling approaches to the same environmental system than the exclusive use of either model type; (2) to some extent, the structure identification and parameter estimation of one type of model can be readily improved by recourse to the modeling results of the other; and (3) reconciliation in the parameter space of the two types of models, based on data or theory, shows superiority over that in the state space. Index words: Activated sludge process, Aquaculture pond, Data-based modeling, Nonlinear system, Parameter estimation, Recursive estimation algorithm, System identification, Theory-based modeling, Time-varying parameter, Uncertainty analysis Modeling Environmental Systems under Uncertainty: Towards a Synthesis of Data-based and Theory-based Models
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