| dc.contributor.author | Abdeh-Kolahchi, Abdolnabi. | en_US |
| dc.date.accessioned | 2014-10-21T12:37:39Z | |
| dc.date.available | 2006 | |
| dc.date.issued | 2006 | en_US |
| dc.identifier.other | AAINR20556 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/54855 | |
| dc.description | Monitoring groundwater aquifers for possible sources of contamination is an important aspect of water resource management. The design of monitoring networks has been one of the key concerns of researchers who deal with the management of groundwater quality. To control, prevent, and remediate groundwater contamination, large number of monitoring well locations is required in a 3-dimensional transient system. This is associated with significant installation, operational and implementation costs. Therefore, a method, which can identify an optimal number of monitoring wells, is useful in saving costs and for effective monitoring of the plume concentration and movement. A state of the art groundwater monitoring network design, which combines groundwater flow and transport results with a Genetic Algorithm (GA) optimization procedure to identify optimal monitoring well location is presented in this study. The proposed sequential network design approach differs from other monitoring network designs by placing the emphasis on maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. It also addresses the issue of enhancing modelling accuracy when the hydrogeologic and hydrochemical data such as contaminant concentration measurement data are sparse. The proposed methodology aims at tracking the pollutant plume movement by sequentially designing and then implementing an optimal monitoring network. Each design and its implementation are followed by a limited period of additional concentration measurements at the monitoring locations. This additional information is later used for a new design of the monitoring network. | en_US |
| dc.description | The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases and this makes it difficult to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach is capable of finding the global optimal solution to a monitoring network design problem involving 18.4 E 18 solutions. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values are specified. The sensitivity of the solution to random numbers, crossover, mutation and elitism is also studied. | en_US |
| dc.description | The performance of this proposed methodology is first validated using a hypothetical site and then evaluated using an existing and monitored Skrydstrup waste disposal site in South Jutland, Denmark. The results from the proposed sequential groundwater monitoring network design using limited data compare very favorably with the observed field measurements. | en_US |
| dc.description | Thesis (Ph.D.)--Dalhousie University (Canada), 2006. | en_US |
| dc.language | eng | en_US |
| dc.publisher | Dalhousie University | en_US |
| dc.publisher | | en_US |
| dc.subject | Engineering, Civil. | en_US |
| dc.title | Optimal dynamic monitoring network design for reliable tracking of contaminant plumes in an aquifer system. | en_US |
| dc.type | text | en_US |
| dc.contributor.degree | Ph.D. | en_US |
