  Streamlined weirs which are a nature-inspired type of weir have gained
tremendous attention among hydraulic engineers, mainly owing to their
established performance with high discharge coefficients. Computational fluid
dynamics (CFD) is considered as a robust tool to predict the discharge
coefficient. To bypass the computational cost of CFD-based assessment, the
present study proposes data-driven modeling techniques, as an alternative to
CFD simulation, to predict the discharge coefficient based on an experimental
dataset. To this end, after splitting the dataset using a k fold cross
validation technique, the performance assessment of classical and hybrid
machine learning deep learning (ML DL) algorithms is undertaken. Among ML
techniques linear regression (LR) random forest (RF) support vector machine
(SVM) k-nearest neighbor (KNN) and decision tree (DT) algorithms are studied.
In the context of DL, long short-term memory (LSTM) convolutional neural
network (CNN) and gated recurrent unit (GRU) and their hybrid forms such as
LSTM GRU, CNN LSTM and CNN GRU techniques, are compared using different error
metrics. It is found that the proposed three layer hierarchical DL algorithm
consisting of a convolutional layer coupled with two subsequent GRU levels,
which is also hybridized with the LR method, leads to lower error metrics. This
paper paves the way for data-driven modeling of streamlined weirs.
