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Simultaneous identification of the number, location and release intensity of groundwater contamination sources based on simulation optimization and ensemble surrogate model
In most instances, the number, location and release intensity of groundwater contamination sources (GCSs) are all unknown. The 0-1 mixed integer nonlinear programming optimization model (0-1 MINPOM) used previously could only identify the location and release intensity for GCSs. Thus a 0-1 MINPOM was improved and applied to simultaneously identify the number, location and release history of GCSs. In addition, to reduce the time of calling the simulation model for iterative calculation, the deep belief neural network (DBNN), long short-term memory network (LSTM) and Kriging method were applied to establish an ensemble surrogate model of the simulation model to replace the simulation model for iterative calculation. The results show that compared with the three single surrogate models, the ensemble surrogate model has the highest approximation accuracy to the simulation model, and could save 99% of the calculation time when iterative calculation was performed in place of the simulation model. The improved 0-1 MINPOM could identify the number, location, and release history of GCSs simultaneously. The accuracy of identifying the number of GCSs was 100%, and the accuracies of identifying the locations and release history were above 95.12% and 83.51%, respectively. HIGHLIGHTS A 0-1 mixed integer nonlinear programming optimization model (0-1 MINPOM) was improved and applied to the identification of GCSs.; The number, location, and release history of GCSs were identified simultaneously based on the improved 0-1 MINPOM.; The DBNN, LSTM and Kriging methods were applied to establish an ensemble surrogate.; The ensemble surrogate was linked to the improved 0-1 MINPOM for iterative calculation.;
Simultaneous identification of the number, location and release intensity of groundwater contamination sources based on simulation optimization and ensemble surrogate model
In most instances, the number, location and release intensity of groundwater contamination sources (GCSs) are all unknown. The 0-1 mixed integer nonlinear programming optimization model (0-1 MINPOM) used previously could only identify the location and release intensity for GCSs. Thus a 0-1 MINPOM was improved and applied to simultaneously identify the number, location and release history of GCSs. In addition, to reduce the time of calling the simulation model for iterative calculation, the deep belief neural network (DBNN), long short-term memory network (LSTM) and Kriging method were applied to establish an ensemble surrogate model of the simulation model to replace the simulation model for iterative calculation. The results show that compared with the three single surrogate models, the ensemble surrogate model has the highest approximation accuracy to the simulation model, and could save 99% of the calculation time when iterative calculation was performed in place of the simulation model. The improved 0-1 MINPOM could identify the number, location, and release history of GCSs simultaneously. The accuracy of identifying the number of GCSs was 100%, and the accuracies of identifying the locations and release history were above 95.12% and 83.51%, respectively. HIGHLIGHTS A 0-1 mixed integer nonlinear programming optimization model (0-1 MINPOM) was improved and applied to the identification of GCSs.; The number, location, and release history of GCSs were identified simultaneously based on the improved 0-1 MINPOM.; The DBNN, LSTM and Kriging methods were applied to establish an ensemble surrogate.; The ensemble surrogate was linked to the improved 0-1 MINPOM for iterative calculation.;
Simultaneous identification of the number, location and release intensity of groundwater contamination sources based on simulation optimization and ensemble surrogate model
Jiuhui Li (author) / Zhengfang Wu (author) / Wenxi Lu (author) / Hongshi He (author)
2022
Article (Journal)
Electronic Resource
Unknown
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