# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 3 Results and Discussion
### Experiments in the Measurement of River Velocity and Discharge
#### 3.3.1 Experiment in an Artificially Repaired River
For individual velocity measurement points, as shown in Figure 10c, the relative errors of the vertical mean velocity are maintained below 10% at points 2, 3, 4, 5, 9, and 10, and identical \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Indictors**} & \multirow{2}{*}{**Points**} & \multicolumn{4}{c}{**Measured Values/(m/s)**} & \multicolumn{4}{c}{**Relative Error/(\%)**} \\ \cline{3-11}  & & **Current** & \multirow{2}{*}{**GTM**} & \multirow{2}{*}{**FFT**} & \multirow{2}{*}{**DIS-G**} & \multirow{2}{*}{**New**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**GTM**} & \multirow{2}{*}{**FFT**} & \multirow{2}{*}{**FD-DIS-G**} & \multirow{2}{*}{**New**} \\  & & **Meter** & & & & & & & & & \\ \hline \multirow{9}{*}{Vertical} & No. 1 & 0.69 & 0.60 & 0.75 & 0.05 & 0.81 & 13.04\% & 8.70\% & 92.75\% & 17.39\% \\  & No. 2 & 0.97 & 0.91 & 1.19 & 0.15 & 1.00 & 6.19\% & 22.68\% & 84.54\% & 3.09\% \\ \cline{1-1}  & No. 3 & 0.97 & 0.83 & 1.06 & 0.78 & 1.02 & 14.43\% & 8.80\% & 19.59\% & 5.15\% \\ \cline{1-1}  & No. 4 & 0.97 & 0.87 & 1.04 & 1.48 & 0.93 & 10.31\% & 7.22\% & 52.58\% & 4.12\% \\ \cline{1-1}  &  No. 5 & 0.87 & 0.68 & 0.98 & 1.42 & 0.95 & 21.84\% & 12.64\% & 63.22\% & 9.20\% \\ \cline{1-1}  & No. 6 & 0.63 & 0.53 & 0.53 & 1.26 & 0.74 & 15.87\% & 15.87\% & 100.00\% & 17.46\% \\ \cline{1-1}  &  No. 7 & 0.69 & 0.68 & 0.56 & 1.00 & 0.56 & 1.45\% & 18.84\% & 44.93\% & 18.84\% \\ \cline{1-1}  & No. 8 & 0.58 & 0.65 & 0.51 & 0.90 & 0.51 & 12.07\% & 12.07\% & 55.17\% & 12.07\% \\ \cline{1-1}  & No. 9 & 0.52 & 0.37 & 0.73 & 0.88 & 0.47 & 28.85\% & 40.38\% & 69.23\% & 9.62\% \\ \cline{1-1}  & No. 10 & 0.40 & 0.24 & 0.64 & 0.60 & 0.44 & 40.00\% & 60.00\% & 50.00\% & 10.00\% \\ \hline \multicolumn{1}{l}{Discharge/(m\({}^{3}\)/s)} & 8.19 & 7.11 & 8.94 & 9.56 & 8.37 & 13.19\% & 9.16\% & 16.73\% & 2.20\% \\ \cline{1-1}  & Mean velocity/(m/s) & 0.68 & 0.59 & 0.74 & 0.79 & 0.69 & 13.24\% & 8.82\% & 16.18\% & 1.47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of measured results and errors of different methods in Agu. Figure 10: The results and analysis of different methods. **(a)** Results of the total discharge; **(b)** results of the mean velocity; **(c)** errors of the vertical mean velocity; **(d)** errors of the total discharge and mean velocity. to those obtained by the FFT method at velocimetry points 7 and 8, and the relative errors are kept within the range of 12% to 19%, and the high accuracy of the measurements is still maintained even in the case of the most serious influence of the illumination, which confirms that it has good robustness and stability. In contrast, the GTM and FFT methods perform at a moderate level, while the measurement values and error fluctuations of FD-DIS-G are the largest, indicating that the stability is poor. The error of one velocimetry point is as high as 100%, which is attributed to the sensitivity to environmental noisy conditions of the FD-DIS-G method, and it can be clearly seen that the presence of a significant uneven illumination in the video of the river leads to a large measurement deviation of FD-DIS-G at each point. Its robustness needs to be improved. Beyond that, the running time of the FD-DIS-G method is the shortest, only 32.75 s, followed by the BGT method (50.96 s) and the FFT method (67.03 s), while the time of the new proposed method is 115.43 s, which is much slower than the other methods. Deep Learning takes a long time to learn the data during the training phase, but after the training is completed, its advantage for improving precision is obvious, so the running efficiency of the proposed method can be ignored in actual measurement work.