# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 4 Conclusions  
A river velocity and discharge measurement method based on improved ShuffleNetV2 is proposed in this study. It utilizes image classification to detect the river texture angle without image processing, and enhances the ability to extract image information in complex river scenes by adjusting and optimizing the network structure and introducing the BAM attention mechanism, which effectively solves the problems of traditional STIV in complex noisy environments such as accuracy and robustness. The training experiment results of the improved ShuffleNetV2 on the dataset show that the TOP1 accuracy is 64.69%, and the TOP5 accuracy is 90.56%. The three sets of experiments indicate that the river discharge and mean velocity are the closest to the true values measured by the current meter. Specifically, the relative errors of the discharge and mean velocity in the Agu artificial river are 2.20% and 1.47%, respectively. In the Panxi and Mengxing natural rivers, the measured relative errors in discharge are 3.40% and 2.37%, respectively, and the measured relative errors in mean velocity are 3.64% and 1.87%, respectively. Compared with the existing BGT, FFT, and FD-DIS-G methods, the proposed method is more effective and stable and confirms the potential of deep learning technology in the hydrological field. It not only provides a new technical means, but also promotes the construction and development of intelligent hydrology. However, the proposed method still has some limitations in practical application. Future research should focus on collecting more diversified river video image data to cover different geographical features and climatic conditions, expanding the scope and variety of datasets, and conducting validation experiments in more river environments to further improve the generalization ability and adaptability of the model. In addition, we have only researched rivers with turbid water and a flow rate of about 1 m/s. Further work can be attempted to apply this method to cases with faster flow rates or clearer water to verify the advantages of the method. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Indicators**} & \multirow{2}{*}{**Points**} & \multicolumn{4}{c}{**Measured Values/(m/s)**} & \multicolumn{4}{c}{**Relative Error**} \\ \cline{3-8}  & & **Current** & **GTM** & **FFT** & **New** & **Method** & **GTM** & **FFT** & **New** \\  & & **Meter** & **GTM** & **FFT** & **Method** & **GTM** & **FFT** & **Method** \\ \hline \multirow{8}{*}{Vertical mean} & No. 1 & 0.45 & 0.21 & 0.24 & 0.55 & 53.33\% & 46.66\% & 22.22\% \\  & No. 2 & 0.96 & 1.20 & 1.23 & 0.70 & 25.00\% & 28.13\% & 27.08\% \\  & No. 3 & 1.28 & 0.88 & 1.25 & 0.97 & 31.25\% & 2.34\% & 24.22\% \\  & No. 4 & 1.40 & 0.85 & 1.23 & 1.13 & 39.29\% & 12.14\% & 19.29\% \\  & No. 5 & 1.38 & 0.78 & 1.20 & 1.21 & 43.48\% & 13.04\% & 12.32\% \\  & No. 6 & 1.38 & 1.46 & 1.22 & 1.23 & 5.80\% & 11.59\% & 10.87\% \\ \cline{1-1}ity/(m/s) & No. 7 & 1.35 & 2.04 & 1.22 & 1.22 & 51.11\% & 9.63\% & 9.63\% \\  & No. 8 & 1.40 & 1.05 & 1.32 & 1.37 & 25.00\% & 5.71\% & 2.14\% \\  & No. 9 & 1.21 & 1.23 & 1.08 & 1.43 & 1.65\% & 10.74\% & 18.18\% \\  & No. 10 & 0.63 & 0.42 & 0.52 & 1.29 & 33.33\% & 17.46\% & 104.76\% \\ \hline Discharge/(m\({}^{3}\)/s) & 88.20 & 77.02 & 80.69 & 86.11 & 12.68\% & 8.51\% & 2.37\% \\ Mean velocity/(m/s) & 1.07 & 0.94 & 0.98 & 1.05 & 12.15\% & 8.41\% & 1.87\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of measured results and errors of different methods in Mengxing. **Author Contributions:** Conceptualization, R.L. and J.W. ; methodology, R.L. ; validation, R.L., J.J. and J.W. ; formal analysis, R.L. ; investigation, R.L. ; resources, D.H., N.L. and X.P. ; writing--original draft preparation, R.L. ; writing--review and editing, R.L. ; visualization, R.L. ; supervision, J.J. and J.W. ; project administration, J.J. and J.W. ; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.