Estimation of Removal Efficiency for Settling Basins Using Neural Networks and Support Vector Machines
Publication: Journal of Hydrologic Engineering
Volume 13, Issue 3
Abstract
An artificial neural network (ANN) and support vector machines (SVMs) were employed for estimating the removal efficiency of settling basins in canals. The performance of ANN and SVMs was tested using the data from an earlier study carried out by Ranga Raju et al. As compared with the Ranga Raju et al. relationship, the correlation coefficient of ANN as well as SVMs improved from 0.77 to 0.9854 and 0.9853, respectively; whereas the root mean squared error values decreased from 50.66 to 5.712 and 5.7366, respectively. Between SVMs and ANN, SVMs’ performance was found to be better due to its use of the principle of structural risk minimization in formulating cost functions and the use of quadratic programming during model optimization. These advantages led to a unique optimal solution as compared to conventional neural network models.
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Published In
Journal of Hydrologic Engineering
Volume 13 • Issue 3 • March 2008
Pages: 146 - 155
Copyright
© 2008 ASCE.
History
Received: Nov 5, 2006
Accepted: Jun 12, 2007
Published online: Mar 1, 2008
Published in print: Mar 2008
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