A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design: part II. Model application.
A new stochastic optimization model under modeling uncertainty (SOMUM) and parameter certainty is applied to a practical site located in western Canada. Various groundwater remediation strategies under different significance levels are obtained from the SOMUM model. The impact of modeling uncertainty (proxy-simulator residuals) on optimal remediation strategies is compared to that of parameter uncertainty (arising from physical properties). The results show that the increased remediation cost for mitigating modeling-uncertainty impact would be higher than those from models where the coefficient of variance of input parameters approximates to 40%. This provides new evidence that the modeling uncertainty in proxy-simulator residuals can hardly be ignored; there is thus a need of investigating and mitigating the impact of such uncertainties on groundwater remediation design. This work would be helpful for lowering the risk of system failure due to potential environmental-standard violation when determining optimal groundwater remediation strategies.
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Department of Civil Engineering, Faculty of Engineering, Architecture and Science, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada M5B 2K3. li.he@ryerson.ca
Huang
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GH
Lu
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D002170:Canada; D052918:Environmental Restoration and Remediation / Q000379:methods*; D015233:Models, Statistical; D013269:Stochastic Processes; D018508:Water Purification / Q000379:methods*