PREDICTION OF SCOUR DEPTH IN DOWNSTREAM OF SKI-JUMP SPILLWAYS USING SOFT COMPUTING TECHNIQUES

Mohammad K. Ayoubloo, Hazi Md. Azamathulla, Zulfequar Ahmad, Aminuddin Ab. Ghani, Javad Mahjoobi, and Amin Rasekh

Keywords

CART, model tree, scour, soft computing, spillways, svm, neural networks

Abstract

Scour of bed in downstream of a ski-jump spillway is a critical phenomenon that can endanger the spillway stability. The present paper deals with application of soft computing techniques like classiļ¬cation and regression tree (CART), support vector machine (SVM) and M5 for the prediction of downstream scour of the spillways. The results of testing data set present CART model as the best among the other computing methods. The results of models showed that CART produces better prediction of the scour in the downstream of the spillway compared to other techniques. However, the other techniques are better than the available empirical relationships for the prediction of scour.

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