Non-deterministic modelling of logical queries in deductive databases

1987 ◽  
Vol 16 (3) ◽  
pp. 60-72 ◽  
Author(s):  
Hussien Aly ◽  
Z. Meral Ozsoyoglu
Keyword(s):  
Deductive Databases ◽  
Deterministic Modelling

Incorporating concepts from physical theory into stochastic modelling of urban runoff pollution

1998 ◽  
Vol 37 (1) ◽  
pp. 179-185
Author(s):  
Morten Grum
Keyword(s):  
Stochastic Model ◽  
Continuous Time ◽  
Measurement Errors ◽  
Physical Theory ◽  
Oxygen Demand ◽  
Stochastic Modelling ◽  
Urban Runoff ◽  
Primary Input ◽  
Deterministic Modelling ◽  
Runoff Pollution

On evaluating the present or future state of integrated urban water systems, sewer drainage models, with rainfall as primary input, are often used to calculate the expected return periods of given detrimental acute pollution events and the uncertainty thereof. The model studied in the present paper incorporates notions of physical theory in a stochastic model of water level and particulate chemical oxygen demand (COD) at the overflow point of a Dutch combined sewer system. A stochastic model based on physical mechanisms has been formulated in continuous time. The extended Kalman filter has been used in conjunction with a maximum likelihood criteria and a non-linear state space formulation to decompose the error term into system noise terms and measurement errors. The bias generally obtained in deterministic modelling, by invariably and often inappropriately assuming all error to result from measurement inaccuracies, is thus avoided. Continuous time stochastic modelling incorporating physical, chemical and biological theory presents a possible modelling alternative. These preliminary results suggest that further work is needed in order to fully appreciate the method's potential and limitations in the field of urban runoff pollution modelling.

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