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Optimization of Groundwater Long-Term Monitoring Network Optimization of Groundwater Long-Term Monitoring Network with Ant Colony Optimization with Ant Colony Optimization

Title: Optimization of Groundwater Long-Term Monitoring Network Optimization of Groundwater Long-Term Monitoring Network with Ant Colony Optimization with Ant Colony Optimization.
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Name(s): Liu, Xiaoli, author
Chen, Gang, 1969-, professor co-directing dissertation
Ye, Ming, professor co-directing dissertation
Wang, Xiaoqiang, university representative
Hilton, Amy B. Chan, committee member
Huang, Wenrui, 1961-, committee member
Tang, Youneng, committee member
Florida State University, degree granting institution
College of Engineering, degree granting college
Department of Civil and Environmental Engineering, degree granting department
Type of Resource: text
Genre: Text
Doctoral Thesis
Issuance: monographic
Date Issued: 2017
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (125 pages)
Language(s): English
Abstract/Description: Groundwater remediation is conducted in polluted sites to remove contaminants and to restore ground water quality. After remediation goals are achieved, long-term groundwater monitoring (LTM) that can span decades is required to assess the concentration of residual contaminants and to avoid the risk of human health and environment. On large remediation sites, the cost for maintaining a LTM network, collecting samples, conducting water quality lab analysis can be a significant, persistent and growing financial burden for the private entities and government agencies who are responsible for environmental remediation projects. LTM network optimization offers an opportunity to improve the cost-effectiveness of the LTM effort while meeting data accuracy requirements. The optimization includes identifying the redundancy in the monitoring network, and recommending changes to protect against potential impacts to the public and the environment. This study develops a variant ant colony optimization (VACO) method, using ordinary kriging (OK) or inverse distance weighting (IDW) for data interpolation, to identify optimal LTM networks that minimize the cost of LTM by reducing the number of monitoring locations with minimum overall data loss. ACO is a global stochastic search method inspired by the collective problem-solving ability of a colony of ants as they search for the most efficient routes from their nests to food sources. The performance of ACO variant (VACO) developed in this study is evaluated separately in two test cases. In the first case, VACO is used to solve a simplified traveling sales person problem. In the second case, both enumeration method and VACO are employed for optimization of a synthetic long term monitoring network of 73 wells generated from a groundwater transport simulation model. The two sets of test show that the VACO performs well for optimization problems. The VACO is finally adopted for the optimization of a long term monitoring network of 30 wells in Logistic Center, Washington, with the data interpolation methods of inverse distance weighing, ordinary kriging, and modified inverse distance weighing which is developed in this study. The optimization results are analyzed and group of ideal redundant wells identified. The conclusion of this study is summarized at the end, and future work is suggested.
Identifier: FSU_FALL2017_Liu_fsu_0071E_14254 (IID)
Submitted Note: A Dissertation submitted to the Department of Civil and Environmental Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Fall Semester 2017.
Date of Defense: November 17, 2017.
Keywords: ant cology optimization, convergence, ground water long term monitoring network, iteration, spatial optimization, swarm intelligence
Bibliography Note: Includes bibliographical references.
Advisory Committee: Gang Chen, Professor Co-Directing Dissertation; Ming Ye, Professor Co-Directing Dissertation; Xiaoqiang Wang, University Representative; Amy Chan Hilton, Committee Member; Wenrui Huang, Committee Member; Youneng Tang, Committee Member.
Subject(s): Environmental engineering
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_FALL2017_Liu_fsu_0071E_14254
Owner Institution: FSU

Choose the citation style.
Liu, X. (2017). Optimization of Groundwater Long-Term Monitoring Network Optimization of Groundwater Long-Term Monitoring Network with Ant Colony Optimization with Ant Colony Optimization. Retrieved from http://purl.flvc.org/fsu/fd/FSU_FALL2017_Liu_fsu_0071E_14254