The data-based mechanistic approach to the modelling, forecasting and control of environmental systems

The paper presents a unified approach to the modelling, forecasting and control of natural and man-made environmental systems. The modelling approach exploits the author’s Data-Based Mechanistic (DBM) modelling philosophy, combined with powerful methods of recursive statistical estimation. These provide the basis for two major stages of model building: first, the critical evaluation of the overparameterised simulation models that are currently the most common vehicle used in environmental planning and management studies; and second, the adaptive, databased estimation of parsimonious, top-down models that can be used for adaptive forecasting and data ssimilation, as well as operational control and management system design. The associated control system design methodology is based on the Non-Minimal State Space (NMSS) approach to the design of Proportional-IntegralPlus (PIP) control systems, based on the DBM models obtained at the previous modelling stage.