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Surrogate-based source identification of DNAPLs-contaminated groundwater |
HOU Ze-yu1,2, LU Wen-xi1,2, WANG Yu1,2 |
1. Key Laboratory of Groundwater Resources and Environment Ministry of Education, Jilin University, Changchun 130021, China;
2. College of Environment and Resources, Jilin University, Changchun 130021, China |
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Abstract Groundwater contamination source identification (GCSI) is critical for taking effective actions in designing remediation strategies, estimating risks, and confirming responsibility. Surrogate-based simulation-optimization technique was applied to source identification and parameter estimation of DNAPLs-contaminated aquifer in this article. The results showed that:1) kernel extreme learning machines (KELM) surrogate model approximated the simulation model accurately. It could simulate the input/output relationship of the simulation model with most of the relative errors less than 5%, and the mean relative error was only 2.98%; 2) Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and maintained high computation accuracy, the identification time was reduced to 3hours from 83days; 3) Simulated annealing-based particle swarm optimization algorithm is efficient in searching the global optimal solution of the nonlinear programming optimization model, and avoiding the optimization process trapping into local optimum.
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Received: 04 June 2018
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