Source identification of sudden contamination based on the parameter uncertainty analysis

Abstract

It is important to identify the source information after a sudden water contamination incident occurs in a water supply system. The accuracy of the simulation model's parameters determines the accuracy of the source information. However, it is difficult to obtain the true value of these parameters by existing methods, so reduction of the errors caused by the uncertainty of these parameters is a crucial problem. A source identification framework which considers the uncertainty of the model's sensitive parameters and combines Bayesian inference and Markov Chain Monte Carlo (MCMC) algorithms simulation is established, and the South-to-North Water Diversion Project is taken as the case study in this paper. Compared with a framework which does not consider the uncertainty of the model's parameters, the proposed framework could solve the error caused by the wrong choice of model parameters and obtain more accurate results. In addition, the proposed framework based on traditional MCMC and that based on the Delayed Rejection and Adaptive Metropolis (DRAM-MCMC) are compared to prove that the DRAM-MCMC is more convergent and accurate. Lastly, the proposed framework based on DRAM-MCMC is proved to solve the problem with high practicality and generality in the studied long distance water diversion project.