Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit

TL;DR

This paper introduces a hybrid deep learning model combining convolutional and gated recurrent units, coupled with linear regression, to accurately predict discharge coefficients of streamlined weirs, reducing reliance on computationally expensive CFD simulations.

Contribution

It proposes a novel hybrid deep learning approach, integrating CNN and GRU layers with linear regression, for efficient and accurate discharge coefficient prediction of streamlined weirs.

Findings

Hybrid CNN-GRU-LR model outperforms classical ML algorithms.

The proposed model achieves lower error metrics in predictions.

Data-driven approach offers a computationally efficient alternative to CFD.

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, leads to lower error metrics. This paper paves the way for data-driven modeling of streamlined weirs.