We have been using an innovative computational approach for optimal groundwater management which involves use of artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict a particular aspect of the outcome of the flow and transport simulation. Then the.GA directs a search, based on the mechanics of genetics and natural selection, through possible management solutions, in this case patterns or realizations of pumping. These pumping realizations are presented to the trained ANN which predicts the outcome of the pumping realizations. The primary advantages of the ANN approach are parallel processing for the flow and transport simulations and the ability to ''recycle'' or reuse the base of knowledge formed by these flow and transport simulations.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Network dissection of neural networks used in optimal groundwater remediation


    Contributors:
    L. L. Rogers (author) / V. M. Johnson (author) / F. U. Dowla (author)

    Publication date :

    1992


    Size :

    17 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English





    An Enhanced Remediation Technology for Contaminated Groundwater

    Hu, H.T. | British Library Conference Proceedings | 2013


    Forensic Investigation and Remediation of Pavement Performance Affected by Groundwater Seepage

    Daoulas, John / Elfino, Mohamed / Nair, Harikrishnan et al. | Transportation Research Record | 2011


    NATURAL ATTENUATION OF TRICHLOROETHYLENE AND DICHLOROETHYLENE AT A GROUNDWATER REMEDIATION SITE

    Egusa, N. / Kakuo, T. / Hirata, T. et al. | British Library Online Contents | 2004


    Artificial neural networks for the prediction of groundwater nitrate contamination

    Stylianoudaki, Christina / Trichakis, Ioannis / Karatzas, George P. | BASE | 2019

    Free access