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Efficacy of Tsallis entropy for velocity estimation in an alluvial channel under different experimental scenarios

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Abstract

A comprehensive understanding of velocity distribution is the fundamental information hydraulic engineers need to estimate an alluvial channel's discharge and stage characteristics. This study examines the Tsallis entropy approach for estimating streamwise velocity patterns in open channels. Entropy, which quantifies system uncertainty, has been applied in hydraulic research to account for variables such as shear strength, silt content, and flow velocities. However, its applicability to non-uniform channel sections remains unexplored. In the current work, the velocities estimation under various experimental conditions was calculated using the Tsallis entropy approach, wherein the random variable employed for constructing the velocity estimations was the time-averaged normalised velocity. This study considered two experimental conditions: (1) channels with attached spurs under seepage and non-seepage conditions and (2) channels with a 31-degree bank slope with and without an upstream pit. The velocity pattern obtained closely corresponds to the experimental data, exhibiting significant accuracy. However, it should be noted that the accuracy of the velocity pattern is slightly diminished in the region near the spur field when the \(y/D\) value is below \(0.3.\) The difference can be caused by factors such as the area's non-uniform cross-section, sediment interaction along the bed, and secondary currents, which ultimately affect the velocity profile.

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Data availability

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

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M.R. developed the methodology and wrote the manuscript. H.K.P. and S.A. did the experiments. B.K. supervised the work. All authors reviewed the manuscript.

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Correspondence to Bimlesh Kumar.

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Roy, M., Patel, H.K., Arora, S. et al. Efficacy of Tsallis entropy for velocity estimation in an alluvial channel under different experimental scenarios. Stoch Environ Res Risk Assess 38, 1035–1049 (2024). https://doi.org/10.1007/s00477-023-02612-z

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