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CivilComp Proceedings
ISSN 17593433 CCP: 82
PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: B.H.V. Topping
Paper 47
Alternative NeuroNets to Estimate Spillway Scour H.M. Azmathullah+, M.C. Deo+ and P.B. Deolalikar+
+Central Water and Power Research Station, Pune, India
H.M. Azmathullah, M.C. Deo, P.B. Deolalikar, "Alternative NeuroNets to Estimate Spillway Scour", in B.H.V. Topping, (Editor), "Proceedings of the Eighth International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering", CivilComp Press, Stirlingshire, UK, Paper 47, 2005. doi:10.4203/ccp.82.47
Keywords: scour depths, ANN, ANFIS, RBF, skijump buckets, spillway scour.
Summary
Artificial neural networks are associated with difficulties like the lack of success for a
given problem and the unpredictable level of accuracy that can be achieved. In every
new application it therefore becomes necessary to check their usefulness visávis
the traditional methods and also to ascertain their performance by trying out
different combinations of network architectures and learning schemes. The present
study was directed in this direction and it involved the problem of scour depth
prediction for skijump type of spillways. It differs from the previous works on
hydraulic scour in that it pertains to the scour at the skijump bucket type of
spillways and uses field measurements (rather than the controlled laboratory ones
involved in most of the earlier studies) to train the networks and also evaluates
performance of different network configurations and learning mechanisms. The
network architectures considered are the regular feed forward back propagation
trained network using the standard error back propagation (FFBP) as well as the cascade
correlation (FFCC) training schemes, the relatively less used configurations of radial
basis function (RBF) and adaptive neurofuzzy inference system (ANFIS).
Various investigators over a period of many decades in the past have given empirical formulae based on laboratory as well as prototype observations in order to predict the scour. However the problem of scour prediction has remained inconclusive, owing partly to the complexity of the phenomenon involved and partly to the limitation of the analysis tool used to synthesize the measured data, which is generally the statistical regression. Considering the fact that direct prototype data would be more reliable than those belonging to hydraulic models, a survey of available works reporting such observations only was made. Considering that many traditional prediction formulae, including those due to Veronese [1], Wu [2], Martins [3], Incyth [4] are based only on q and H_{1} neural networks with these two input nodes and one output node of the scour depth were developed. In total there were 91 input  output pairs formed from the published data. Eighty percent of the input  output patterns chosen randomly was used for network training while remaining ones were used for testing. All developed networks were tested with the help of the testing pairs, not seen by the networks. The test results were qualitatively compared by drawing scatter plots and quantitatively assessed using the error measures of correlation coefficient (R), average errors (AE), and average absolute deviation (). Such testing was also performed in respect of a new regression equation derived by the authors on the basis of compiled data set. It was found that if the prediction of scour downstream of a skijump buckets is made using only the regression formulae then the new equation derived by the authors based on compilation of past field data can be recommended in preference to the traditional equations by Veronese, Wu, Martinsa and Incyth. Among these prevailing formulae the Veronese one overpredicts the actual scour while the Incyth, Wu and Martins formulae under predict the same. The usual feed forward networks of FFBP and FFCC showed a tendency to overestimate at lower values, unlike the radial basis function (RBF) and the adaptive neurofuzzy inference schemes (ANFIS) . The RBF and the ANFIS yielded more or less similar predictions with the ANFIS producing middleranged estimates in a slightly better way than the RBF. A common application of four different error criteria confirmed the best performance of the ANFIS in this particular mapping problem. The treatment to nonlinearities in the scour data meted out by the ANFIS approach worked much better than the other schemes. The scour data thus seem to be more amenable to fuzzy if then rules rather than crisp value processing in RBF or FFBP networks. The ANFIS ensures localized functioning of the transfer function as against the globalized one of a general FFBP resulting in smaller number of values participating in the mapping process, which in turn requires limited data for training. This could also be another reason for more acceptable performance of ANFIS in the present case. This study thus showed that the traditional equationbased methods of predicting design scour downstream of a skijump bucket could better be replaced by the neural network and similar soft computing schemes. References
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