USE OF ARTIFICIAL NEURAL NETWORKS TO EVALUATE THE EFFECTIVENESS OF RIVERBANK FILTRATION

RAY C. / SAHOO G. B. / WANG J. Z. / et al. - ARTICLE DE PERIODIQUE - 2005

This paper illustrates the development and application of three types of artificial neural Network (ANNs) to estimate the effectiveness of two Riverbank Filtration facilities in the US. The feed-forward back-propagation network (BPN) and radial basis function network (RBFN) model prediction results produced excellent agreement with measured data at a correlation coefficient above 0.99 for filtrate water quality parameters, including temperature as well as turbidity, heterotrophic bacteria, and coliform removal.