Estimation of removal efficiency for settling basins using neural networks and support vector machines

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' perforinance 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.