Stochastic risk assessment of water quality using advection dispersion equation and Bayesian approximation: A case study

ABSTRACT

The influence of various heterogeneous parameters, stochastic uncertain factors, and pollutant particles from the industrial effluents in the water system is investigated using advection dispersion equation (ADE) and the Bayesian approximation. Here, the decay coefficient is decomposed into the exact part and the deviation part. The coefficient is used to find out the errors and deviation in decay during the flow of pollutants. Two Bayesian models are developed to analyze the posterior distributions and to find out the Bayes factor for the stochastic covariance estimation. The Bayesian calibration focused the characteristics of both on mechanistic and statistical approximation. The efficiency and accuracy of the developed models are checked from the results obtained on the basis of the confidence interval. Markov chain Monte Carlo simulation is used to acquire the convergence point of parameters for the posterior estimation. The stochastic covariance or white noise represents the effect of random factors on the river system. The analysis revealed that the rate of decay is dependent upon the duration and distance traveled by the pollutants. The collaboration of ADE and Bayesian approximation encourage the water-quality management and environmental modeling.

KEYWORDS:

• decay coefficient
• dispersion coefficient
• expectation
• Bayesian approximation
• MCMC