Optimal groundwater remediation design using an Adaptive Neural Network Genetic Algorithm

2006 ◽  
Vol 42 (5) ◽  
Author(s):  
Shengquan Yan ◽  
Barbara Minsker
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Groundwater Remediation ◽  
Adaptive Neural Network ◽  
Remediation Design

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

1997 ◽  
Vol 1 (2) ◽  
pp. 345-356 ◽  
Author(s):  
Z. Rao ◽  
D. G. Jamieson
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Hybrid Algorithm ◽  
Large Scale ◽  
Groundwater Remediation ◽  
Injection Wells ◽  
Representative Problem

Abstract. 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.

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