Modelling Floodplain Boundary Shear Distribution in Two-Stage Meandering Channels Using Machine Learning Techniques
DOI:
https://doi.org/10.66132/ngce20250404Keywords:
Boundary shear distribution, Two-stage meandering channel, Gaussian Process Regression, Extreme Learning Machine, Relevance Vector MachineAbstract
The distribution of boundary shear stress plays a pivotal role in addressing various river engineering challenges, including flood control, sediment transport, and riverbank stabilisation. This study employs three machine learning (ML) techniques, i.e. Gaussian Process Regression (GPR), Extreme Learning Machine (ELM), and Relevance Vector Machine (RVM), to predict boundary shear distribution along the floodplains of a two-stage sinuous channel. Key geometric and hydraulic parameters such as the relative width , depth ratio , sinuosity , slope of the channel bed , and sinuous relative belt width of the two-stage meandering channels are used to create predictive models. A comprehensive comparative analysis was conducted using standard statistical performance metrics. Among the models, the GPR approach demonstrated superior predictive accuracy, achieving a coefficient of determination (R²) of 0.984 and the lowest root mean squared error (RMSE) of 2.0. These findings highlight the potential of GPR as a robust tool for modelling shear stress distribution in complex channel geometries.
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