Recognizing groundwater DNAPL contaminant source and aquifer parameters using parallel heuristic search strategy based on Bayesian approach

Abstract

In this paper, a parallel heuristic search strategy based on Bayesian approach was first proposed for recognizing groundwater DNAPL contaminant source and aquifer parameters (unknown variables). Frequent calls to numerical simulation model effectuated large computational burden during likelihood calculation. Single surrogate system was established to reduce the burden, but it had unavoidable limitations. Thus, we first presented the particle swarm optimization-tabu search hybrid algorithm to construct an optimal combined surrogate system for the simulation model, which assembled Gaussian process, kernel extreme learning machine, support vector regression, and also improved the accuracy of the surrogate system to simulation model. Thereafter, a parallel heuristic search iterative process was first implemented for simultaneous recognition of unknown variables. Each round of iteration involved determination of candidate points and state transitions. The Monte Carlo approach was used widely for selecting candidate point, but it did not readily converge to posterior distribution when the probability density functions were complex. And the search ergodicity was weak. In order to improve the search ergodicity, a DE algorithm with variable mutation rate based on rand-to-best, 1, and bin strategy was first proposed in this paper to determine multiple candidate points. The recognition results were obtained when the iteration process terminated. The accuracy and efficiency of our approaches were demonstrated through a hypothetical case in DNAPLs-contaminated aquifer, and the recognizing accuracy was high. More importantly, the new simulation model based on the recognition results is helpful in calculating future contaminant plume in the aquifer, which can provide credible basis for groundwater contaminant remediation plan design and risk assessment.