Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Chen, Weibin; Sharifrazi, Danial; Liang, Guoxi; Band, Shahab S.; Chau, Kwok Wing; Mosavi, Amir
• Is Part Of: Engineering applications of computational fluid mechanics. Volume 16:Issue 1(2022)
• Page Start: 965
• Page End: 976
• Publication Date: 2022-12-31
• Subjects: Streamlined weirs -- discharge prediction -- deep learning -- machine learning -- deep convolutional neural network -- gated recurrent unit; Computational fluid dynamics -- Periodicals; 620.10640285
• DOI: 10.1080/19942060.2022.2053786
• Languages: English
• ISSNs: 1994-2060

Abstract

Abstract : 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 (i.e. LR-CGRU), leads to lower error metrics. This paper paves the way for data-driven modeling of streamlined weirs.