Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty

Multi-objective optimization of long-term groundwater monitoring network design using a... Journal of Hydrology 534 (2016) 352–363 Contents lists available at ScienceDirect Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty a b,⇑ b,c a b Qiankun Luo , Jianfeng Wu , Yun Yang , Jiazhong Qian , Jichun Wu School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China Huai River Water Resources Commission, Bengbu 233001, China ar ti c l e i nf o su mmary Article history: Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objec- Received 26 October 2015 tives and substantial uncertainty arising from insufficient hydraulic conductivity (K) data. This study Received in revised form 3 January 2016 develops a new multi-objective simulation–optimization model involving four objectives: minimizations Accepted 6 January 2016 of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first Available online 12 January 2016 moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for This manuscript was handled http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrology Elsevier

Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier B.V.
ISSN
0022-1694
eISSN
1879-2707
D.O.I.
10.1016/j.jhydrol.2016.01.009
Publisher site
See Article on Publisher Site

Abstract

Journal of Hydrology 534 (2016) 352–363 Contents lists available at ScienceDirect Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty a b,⇑ b,c a b Qiankun Luo , Jianfeng Wu , Yun Yang , Jiazhong Qian , Jichun Wu School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China Huai River Water Resources Commission, Bengbu 233001, China ar ti c l e i nf o su mmary Article history: Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objec- Received 26 October 2015 tives and substantial uncertainty arising from insufficient hydraulic conductivity (K) data. This study Received in revised form 3 January 2016 develops a new multi-objective simulation–optimization model involving four objectives: minimizations Accepted 6 January 2016 of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first Available online 12 January 2016 moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for This manuscript was handled

Journal

Journal of HydrologyElsevier

Published: Mar 1, 2016

References

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