Artificial Neural Network Application for Predicting Drag Coefficient in Flexible Vegetated Channels

Abstract

Previously numerous equations were developed using conventional methods to estimate vegetal drag coefficient by treating submerged and emergent vegetation independently, there is need to derive a generalized relationship that can be applied irrespective of the vegetation submergence with respect to flow depth. In this regard, the present study uses artificial neural network (ANN) as an advanced tool for prediction of drag coefficient in flexible vegetated channels. The training and testing patterns of the proposed ANN model were based on experimental results from the field and laboratory studies that combined both the submerged and emergent grass. A functional relation based on flow parameters and vegetation properties was derived through the use of dimensional analysis. The ANN model developed herein showed significantly better results in several model performance criteria when applied for verification.