HR: 1340h
AN: H23A-1130    [Abstracts]
TI: Application of Genetic Algorithms To Identify Optimal Groundwater Monitoring Well Locations in 3D
AU: * Abdeh-Kolahchi, A
EM: aabdehko@dal.ca
AF: A. Abdeh-Kolahchi, Department of Civil Engineering, Dalhousie University, Halifax, NS B3J 1Z1 Canada
AU: Satish, M
EM: msatish@dal.ca
AF: A. Abdeh-Kolahchi, Department of Civil Engineering, Dalhousie University, Halifax, NS B3J 1Z1 Canada
AU: Datta, B
EM: bithin@iitk.ac.in
AF: Bithin Datta, Department of Civil Engineering,I.I.T. Kanpur, Kanpur, India India
AB: Monitoring groundwater aquifers for possible sources of contamination is an important aspect of water resources management. The design of monitoring networks has been one of the key concerns of researchers who deal with the management of groundwater quality. Optimal monitoring network design can be beneficial to both groundwater simulation as well as optimization modeling. This paper discusses the applications of various optimization techniques from traditional to global methods for the solution of groundwater monitoring network and groundwater quality management problems. In order to solve optimization-based groundwater management models, various mathematical programming techniques such as linear/nonlinear programming, mixed-integer programming, differential dynamic programming, stochastic programming, as well as global optimization methods such as Genetic Algorithms are used by researchers to obtain optimal solutions for groundwater management. The resent study will also discuss a state of the art method, which combines simulation of groundwater flow and transport with genetic algorithm optimization. In order to ensure that the optimal management strategy is physically acceptable, a simulation model is necessary to simulate the system behavior. The simulation model basically provides solutions that satisfy the equations governing the relevant processes in the system. Thus the simulation models can be used for checking the feasibility of a management strategy. Once the optimization model is formulated, a suitable mathematical programming technique such as genetic algorithm is applied to obtain the optimal solution. This approach not only accounts for the complex and non-linear behavior of the groundwater system, but also identifies the best monitoring strategy under a specific objective function with several constraints. The solution identifies the best location of monitoring wells.
DE: 1800 HYDROLOGY
DE: 1829 Groundwater hydrology
DE: 1831 Groundwater quality
DE: 1832 Groundwater transport
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting