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. / A Dissertation submitted to the Department of Civil and Environmental Engineering in partial fulfillment of
the requirements for the degree of Doctor of Philosophy. / Fall Semester 2017. / November 17, 2017. / ant cology optimization, convergence, ground water long term monitoring network, iteration, spatial optimization,
swarm intelligence / Includes bibliographical references. / 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.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_604985 |
| Contributors | 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 departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (125 pages), computer, application/pdf |
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