The Use of Neural Networks and Genetic Algorithms for Design of Groundwater Remediation Schemes

Z. Rao (1) , D. G. Jamieson (1)

1 Department of Civil Engineering

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.

Domaines: Interfaces continentales, environnement; Océan, Atmosphère; Sciences de la Terre

Z. Rao, D. G. Jamieson. The Use of Neural Networks and Genetic Algorithms for Design of Groundwater Remediation Schemes. Hydrology and Earth System Sciences Discussions, 1997, 1 (2), pp.345-356. ⟨hal-00304404⟩