Binning, Philip John

Bode, Felix

Nowak, Wolfgang

Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability and optimality of any suggested monitoring location or monitoring network.The goal of this project is to develop and establish a concept to assess, design, and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: (1) a high detection probability, which can be reached by maximizing the “field of vision” of the monitoring network; (2) a long early-warning time such that there is enough time left to install countermeasures after first detection; and (3) the overall operating costs of the monitoring network, which should ideally be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, wrapped up within the framework of formal multi-objective optimization. In order to gain insight into the flow and transport physics and statistics that control the optimality of monitoring wells, and thus in order to perform the optimization in a more formal targeted manner, we first use an analytical model based on the 2D steady-state advection-dispersion equation. Monte-Carlo simulation techniques are applied to represent parametric uncertainty. From this, we can obtain maps of contaminant detection probability for all possible placements of one individual monitoring well. Its optimal position is defined by the highest detection probability and describes a limit for meaningful solutions considering additionally early-warning time. Thus, a significant number of potential positions can be excluded from the optimization of entire networks, improving the computational efficiency of network optimization. Finally, we demonstrate that the individual well optima can indeed be found to be part of the results

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Enhanced Multi-Objective Optimization of Groundwater Monitoring NetworksBode, Felix; Binning, Philip John; Nowak, WolfgangPublication date:2014Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Bode, F., Binning, P. J., & Nowak, W. (2014). Enhanced Multi-Objective Optimization of Groundwater MonitoringNetworks. Abstract from NGWA Groundwater SUMMIT 2014, Denver, Colorado, United States.Conference Overview Program Registration Venue and accommodationsExhibit/Sponsorship Opportunites 2013 HighlightsPhilip Binning , Department of Environmental Engineering, Technical University of Denmark, Lyngby, Denmark Wolfgang Nowak , Department of Hydromechanics and Modelling of Hydrosystems, University of Stuttgart, Stuttgart, Germany Enhanced Multi-Objective Optimization of Groundwater Monitoring NetworksTuesday, May 6, 2014: 3:20 p.m.Blake (Westin Denver Downtown)Felix Bode , Department of Hydromechanics and Modelling of Hydrosystems, University of Stuttgart, Stuttgart, Germany Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability and optimality of any suggested monitoring location or monitoring network. The goal of this project is to develop and establish a concept to assess, design, and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: (1) a high detection probability, which can be reached by maximizing the “field of vision” of the monitoring network; (2) a long early-warning time such that there is enough time left to install countermeasures after first detection; and (3) the overall operating costs of the monitoring network, which should ideally be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, wrapped up within the framework of formal multi-objective optimization. In order to gain insight into the flow and transport physics and statistics that control the optimality of monitoring wells, and thus in order to perform the optimization in a more formal targeted manner, we first use an analytical model based on the 2D steady-state advection-dispersion equation. Monte-Carlo simulation techniques are applied to represent parametric uncertainty. From this, we can obtain maps of contaminant detection probability for all possible placements of one individual monitoring well. Its optimal position is defined by the highest detection probability and describes a limit for meaningful solutions considering additionally early-warning time. Thus, a significant number of potential positions can be excluded from the optimization of entire networks, improving the computational efficiency of network optimization. Finally, we demonstrate that the individual well optima can indeed be found to be part of the results. Felix Bode, Department of Hydromechanics and Modelling of Hydrosystems, University of Stuttgart, Stuttgart, Germany Felix Bode is a Ph.D. student in the Department of Hydromechanics and Modelling of Hydrosystems at the University of Stuttgart (Germany). From October 2007 to January 2013 he studied Page 1 of 2Abstract: Enhanced Multi-Objective Optimization of Groundwater Monitoring Netwo...24/11/2014https://ngwa.confex.com/ngwa/2014gws/webprogram/Paper9773.htmlEnvironmental Engineering at the University of Stuttgart. Philip Binning, Department of Environmental Engineering, Technical University of Denmark, Lyngby, Denmark Philip Binning is a Professor and Head of the Education Department of Environmental Engineering at the Technical University of Denmark. Wolfgang Nowak, Department of Hydromechanics and Modelling of Hydrosystems, University of Stuttgart, Stuttgart, Germany JWolfgang Nowak, M.Sc. University of Stuttgart (Germany), Department of Hydromechanics and Modelling of Hydrosystems. See more of: Monitoring and Data ManagementSee more of: Topical Sessions<< Previous Abstract | Next AbstractBrowse/SearchBrowseAlphabetical Listing of SessionsAuthor IndexMeeting InformationWhen:May 04 - 07, 2014Where:Denver, COPage 2 of 2Abstract: Enhanced Multi-Objective Optimization of Groundwater Monitoring Netwo...24/11/2014https://ngwa.confex.com/ngwa/2014gws/webprogram/Paper9773.html

Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks

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Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks

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