Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 1 2 1997 10.5194/hess-1-345-1997 http://www.hydrol-earth-syst-sci.net/1/345/1997/ http://www.hydrol-earth-syst-sci.net/1/345/1997/hess-1-345-1997.html http://www.hydrol-earth-syst-sci.net/1/345/1997/hess-1-345-1997.pdf 345 356 0000-00-00 The Use of Neural Networks and Genetic Algorithms for Design of Groundwater Remediation Schemes Z. Rao D. G. Jamieson Department of Civil Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK The increasing incidence of groundwater pollution has led to recognition of a need to develop objective techniques for designing reniediation schemes. This paper outlines one such possibility for determining how many abstraction/injection wells are required, where they should be located etc., having regard to minimising the overall cost. To that end, an artificial neural network is used in association with a 2-D or 3-D groundwater simulation model to determine the performance of different combinations of abstraction/injection wells. Thereafter, a genetic algorithm is used to identify which of these combinations offers the least-cost solution to achieve the prescribed residual levels of pollutant within whatever timescale is specified. The resultant hybrid algorithm has been shown to be effective for a simplified but nevertheless representative problem; based on the results presented, it is expected the methodology developed will be equally applicable to large-scale, real-world situations.