Cyclic Feedback Updating Approach and Uncertainty Analysis for the Source Identification of DNAPL-Contaminated Aquifers
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 2
Abstract
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.
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Data Availability Statement
All data, models, and code generated or used during the study are available from the corresponding author by request.
Acknowledgments
This study was supported by the National Natural Science Foundation of China (Grant Nos. 41907164, 41807155, and 41672232), and the China Postdoctoral Science Foundation (Grant No. 2018M641780). Special gratitude is given to editors for their efforts on treating and evaluating the work, and the valuable comments of the anonymous reviewers are also greatly acknowledged.
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Journal of Water Resources Planning and Management
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Sep 26, 2019
Accepted: Sep 4, 2020
Published online: Nov 27, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 27, 2021
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