Abstract:
Movement of sediment in the river causes many chang
es in the river bed. These changes
are called bedform. River bedform has significant a
nd direct effects on bed roughness,
flow resistance, and water surface profile. Thus, h
aving adequate knowledge of the
bedform is of special importance in river engineeri
ng. Several methods have been
developed by researchers for estimation of bed form
dimensions. In this investigation,
bedform has been estimated using Artificial Neural
Network (ANN) and Support Vector
Machine (SVM) methods. The results obtained from th
ese two methods were compared
with empirical formulas of Van Rijn. The accuracy o
f the model was evaluated using
(RMSE), (MSRE), (CE), (R
2
) and (RB) statistical parameters. Higher values of
statistical
parameters indicated that the SVM model with RBF ke
rnel function predicted the
bedform more accurately than the other method. The
values calculated for R
2
, RMSE,
MSRE, CE and RB parameters were 0.79, 0.024, 0.066,
0.786, -0.081, respectively.
Comparison of the results of the SVM model with RBF
kernel with other models
indicated that SVM had a higher capability for esti
mating and simulating height of the
bedform than Artificial Neural Networks.
Keywords
: Bed roughness, RBF kernel function, River enginee
ring.
