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| Titel |
The Use of Neural Networks and Genetic Algorithms for Design of Groundwater Remediation Schemes |
| VerfasserIn |
Z. Rao, D. G. Jamieson |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 1, no. 2 ; Nr. 1, no. 2, S.345-356 |
| Datensatznummer |
250000153
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| Publikation (Nr.) |
 copernicus.org/hess-1-345-1997.pdf |
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| Zusammenfassung |
| 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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