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Hypothetical and real case studies were combined to explore the feasibility and effectiveness of a surrogate-based cyclic feedback updating approach for groundwater contamination source identification (GCSI) at dense non-aqueous-phase liquid (DNAPL)-contaminated sites. Support vector regression (SVR), kriging, and kernel extreme learning machine (KELM) models were integrated to build a surrogate model of the multiphase flow simulation model with a high computational efficiency. A mixed homotopy-differential evolution (DE) algorithm is presented to solve the optimization model, in which the integrated surrogate model was embedded, to obtain the identification results, and a cyclic feedback updating process was developed to gradually improve the results. Finally, GCSI uncertainty analysis was conducted using the Monte Carlo method. The results showed that the integrated surrogate model accurately approximates the simulation model, with a mean relative error of only 2.56%. The combination of the homotopy algorithm and DE algorithm provided an effective improvement over the traditional heuristic algorithm, and the mean relative error of the identified source characteristics was limited to 3.28%. GCSI accuracy was significantly improved after the application of the cyclic feedback updating method by reducing the mean relative error of the final identification results to 2.14%. In addition, the probability distribution characteristics of the identification results were obtained via uncertainty analysis to provide a comprehensive and reliable reference for decision makers. [ABSTRACT FROM AUTHOR]

Surrogate-based simulation-optimization techniques are widely applied in developing optimal remediation strategies that increase the efficiency and reduce the cost of surfactant-enhanced aquifer remediation (SEAR) when clearing dense nonaqueous phase liquids (DNAPLs). In such processes, there are many uncertainty factors that may greatly affect the optimization outcome of a SEAR strategy selection. However, previous research on this subject rarely incorporates an uncertainty analysis. This paper presents an uncertainty analysis of both the simulation model and the ensemble surrogate model used to optimize SEAR strategies. Set pair analysis (SPA) and kriging methods were used to build the ensemble surrogate model. The probability distributions of the residuals of the outputs between the ensemble surrogate model and the simulation model, run on 100 testing samples, were analyzed to ascertain the uncertainty of the surrogate model, which was found to be less than 1.5%. The uncertainty of the simulation model was derived by combining a Monte Carlo random simulation with the Sobol' global sensitivity analysis. Finally, a stochastic nonlinear programming model was established to compute the optimal remediation strategies for the remediation target under different confidence levels. This research will allow decision makers to more confidently select the optimal remediation strategy for a given scenario by balancing the reliability of model prediction with the cost of the remediation strategy according to the demands of the project. [ABSTRACT FROM AUTHOR]