Abstract:
In the past two decades, many mathematical water duality models have been developed to simulate physical. chemical. and biological processes occurring in river water. Their possible applications range from identifying in streaming processes
affecting river water quality to forecasting the quality for operational purposes.
It was a common practice to describe problems related to chemical and biological processes in river waters through deterministic differential equations. Since the deterministic model provides a single response for each set of model parameters and initial conditions, there is always some uncertainty, both in the evaluation of field data and in the use of mathematical models to predict the outcome of natural processes. The full representation of the process responses is usually too complicated and may be too costly to develop. Due to inherent variability and randomness in natural processes and their measurements, all these sources of uncertainty could be represented as input forcing terms in the balance equations. The initial conditions may be random, either because of the imperfect real initial conditions or because of the biased measurements. The model coefficients (rate constants) may be random due to variations in measurements.
Number of models have been proposed in recent years which treat water quality processes as stochastic. In the present study a review of the available literature on stochastic water quality modelling' have been made.
