TY - GEN
T1 - Evaluation of filtrate water quality of a river bank filtration facility using neural computing techniques
AU - Sahoo, Goloka Behari
AU - Ray, Chittaranjan
PY - 2005
Y1 - 2005
N2 - Riverbank filtration (RBF) is a natural and low-cost water-treatment process in which contaminants of surface water are removed or degraded as the river water moves through the adjoining alluvial aquifer into the pumping wells. Because river-aquifer interaction is highly nonlinear, time-varying, and spatially-distributed processes that is not easily described by simple mathematical models, application of artificial neural networks (ANNs) is examined to evaluate the effectiveness of a RBF facility at Louisville. Kentucky. USA. Three types of ANNs: feed-forward back-propagation network (BPN), radial basis function network (RBFN), and fuzzy inference system network (FISN) were used in this study. It is shown that BPN and RBFN predicted values were in excellent agreement with the measured values having correlation coefficient above 0.99, whereas FISN was able to predict only temperature and HPC removal of the filtrate water quality with correlation coefficient above 0.97.
AB - Riverbank filtration (RBF) is a natural and low-cost water-treatment process in which contaminants of surface water are removed or degraded as the river water moves through the adjoining alluvial aquifer into the pumping wells. Because river-aquifer interaction is highly nonlinear, time-varying, and spatially-distributed processes that is not easily described by simple mathematical models, application of artificial neural networks (ANNs) is examined to evaluate the effectiveness of a RBF facility at Louisville. Kentucky. USA. Three types of ANNs: feed-forward back-propagation network (BPN), radial basis function network (RBFN), and fuzzy inference system network (FISN) were used in this study. It is shown that BPN and RBFN predicted values were in excellent agreement with the measured values having correlation coefficient above 0.99, whereas FISN was able to predict only temperature and HPC removal of the filtrate water quality with correlation coefficient above 0.97.
UR - http://www.scopus.com/inward/record.url?scp=84872079718&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872079718&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84872079718
SN - 0972741216
SN - 9780972741217
T3 - Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005
SP - 970
EP - 986
BT - Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005
T2 - 2nd Indian International Conference on Artificial Intelligence, IICAI 2005
Y2 - 20 December 2005 through 22 December 2005
ER -