An improved Bayesian approach linked to a surrogate model for identifying groundwater pollution sources

Groundwater pollution source identification (GPSI) provides information about the temporal and spatial distribution of pollution sources and helps decision makers design pollution remediation plans to protect the groundwater environment. The Bayesian approach based on the Markov Chain Monte Carlo (MCMC) approach provides an efficient framework for GPSI. However, MCMC sampling entails multiple model calls to converge to the posterior probability distribution of unknown pollution source parameters and entails a massive computational load if the simulation model is directly called. This study aimed to develop an innovative framework in which an improved MCMC approach was linked to a surrogate model. Sensitivity analysis was incorporated into the MH-MCMC approach, named SAMH-MCMC (sensitivity analysis based Metropolis Hastings-Markov Chain Monte Carlo), to speed up the convergence of the posterior distribution in a novel way to control the search step size. Three computationally inexpensive surrogate models for the simulation model were proposed: support vector regression, Kriging (KRG), and multilayer perceptron, and the most accurate model was chosen. The feasibility and advantages of the developed framework were evaluated and validated through two hypothetical numerical cases with homogenous and heterogeneous media. The proposed approach has strong convergence robustness as it considers the sensitivities of the unknown parameters that characterise groundwater pollution sources and can achieve high identification accuracy. Furthermore, the KRG surrogate model has a higher accuracy than other surrogate models, owing to its linear unbiased estimation characteristic. Overall, the framework developed in this study is a promising solution for identifying groundwater pollution source parameters.