Project Details
Description
Physical, chemical and biological processes involved in the oxygen dynamics in receiving waters are assessed through identification and estimation of stochastic dynamical models.
The dynamics are expressed as a function of solar radiation, precipitation, surface runoff and runoff from urban sewer systems. The goal is a formulation in continuous time, which facilitates a direct physical interpretation and involves known physical laws and parameters in the model.
The models will be used to assess the wate qualilty of the receiving waters, with respect to the planning and management of water quality as well as the sinsitivity to external influences. This will in turn increase the understanding of the complicated processes involved.
The methods used, are the so-called "grey/box" techniques, which combine and exploit the strongest parts of the hitherto most used methodology. Here, known physical differential equations, as well as the data, are used to estimate paramters and possibly unknown processes. This means that non-linear processes can easily be included in the model. as opposed to traditional black-box models. Furthermore, stochastic effects, that any natural system will contain, can be accommodated.
The dynamics are expressed as a function of solar radiation, precipitation, surface runoff and runoff from urban sewer systems. The goal is a formulation in continuous time, which facilitates a direct physical interpretation and involves known physical laws and parameters in the model.
The models will be used to assess the wate qualilty of the receiving waters, with respect to the planning and management of water quality as well as the sinsitivity to external influences. This will in turn increase the understanding of the complicated processes involved.
The methods used, are the so-called "grey/box" techniques, which combine and exploit the strongest parts of the hitherto most used methodology. Here, known physical differential equations, as well as the data, are used to estimate paramters and possibly unknown processes. This means that non-linear processes can easily be included in the model. as opposed to traditional black-box models. Furthermore, stochastic effects, that any natural system will contain, can be accommodated.
Status | Finished |
---|---|
Effective start/end date | 01/04/1994 → 31/05/1997 |
Collaborative partners
- Technical University of Denmark (lead)
- PH-Consult ApS (Project partner)
Funding
- Unknown
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