Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit

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 robust tool predict the coefficient. To bypass computational cost CFD-based assessment, present study proposes data-driven modeling techniques, an alternative CFD simulation, coefficient based on experimental dataset. this end, after splitting dataset using k fold cross validation technique, assessment classical and hybrid machine learning deep (ML DL) algorithms undertaken. Among ML techniques linear regression (LR) random forest (RF) support vector (SVM) k-nearest neighbor (KNN) decision tree (DT) studied. In context DL, long short-term memory (LSTM) convolutional neural network (CNN) gated recurrent unit (GRU) forms such LSTM GRU, CNN GRU compared different error metrics. It found that proposed three layer hierarchical DL algorithm consisting coupled two subsequent levels, also hybridized LR method, leads lower This paper paves way for streamlined weirs.