Heuristic search strategy based on probabilistic and geostatistical simulation approach for simultaneous identification of groundwater contaminant source and simulation model parameters

In this study, a heuristic search strategy based on probabilistic and geostatistical simulation approach is developed for simultaneous identification of groundwater contaminant source and simulation model parameters. The numerical simulation model, which is repeatedly invoked to evaluate the likelihood in Bayesian formula, can be substituted by the surrogate system to reduce the huge computational load. To improve the approximation accuracy of the surrogate system to the simulation model, we employ Entropy Weight method as a novel method to establish a combined surrogate system by combining Gaussian process, support vector regression, and kernel extreme learning machine. A state evaluation (heuristic) function based on Bayesian formula and the surrogate system is introduced to quantify the approximation degree of variables in current state to true values for contaminant sources and simulation model parameters. Thereafter, a heuristic search iterative process related to artificial intelligence is designed for simultaneous identification, which takes full advantage of the guidance and correction role of actual field monitoring data. A multi-vector and variable-step size random walk method is proposed to select the candidate point. A Metropolis formula based on the state evaluation function is constructed, and the result is used as the judging criterion for the state transition. Finally, simultaneous identification results are obtained when the iteration reaches the convergence criteria. The proposed approaches are tested with a numerical case study. The results indicate that the heuristic search strategy can assist in identifying groundwater contaminant source and simulation model parameters simultaneously with high accuracy and efficiency.