## 4 Discussion  
Figure 14 shows the vertical flow velocity distribution that was measured using ADCP-SL at the turbidity measurement position of 1 m upstream, and the horizontal flow velocity distribution that was measured using ADCP-M9 at the center of the channel 1 m downstream. Figure 14: (**a**) Vertical flow velocity distribution and (**b**) horizontal flow velocity distribution. Figure 13: Concentration estimated using the predicted turbidity by the developed model: (**a**) power-law, (**b**) second-order polynomial, and (**c**) first-order polynomial. In the case of the mean velocity distribution in the vertical direction that was measured using ADCP, the section approximately 0.1 m from the water surface and the unmeasured section 0.1 m from the floor were excluded. It appears that the flow velocity decreased as the concentration and turbidity measuring instruments that were installed at 0.35 m from the floor at the upstream point 1 m from the flow velocity measurement point affected the flow. Except for this, the flow velocity distribution in a rectangular channel was observed. In the case of the horizontal flow velocity distribution, the highest flow velocity was observed at the center of the channel. Overall, a slightly higher flow velocity was observed on the left bank side because water was supplied by the pump that was located on the right side. When the vertical and horizontal distributions of the SSC were predicted from the color change of pixels in the images using the developed models, the SSC was found to increase as the water depth increased, and the conventional suspended sediment behavior with no significant difference in the horizontal direction was clearly observed (Figure 15). The proposed models showed excellent predictive power, but the data of the section where sediment was injected were not used for model development. This is because the RGB band values at the same pixel coordinates changed during the injection of sediment because of significant changes in brightness and saturation. In addition, it could be confirmed that blue or green colored water changed to red colored water because of the injection of sediment under the initial condition. However, there were limitations in estimating the slight color changes in red and orange using the RGB values alone (Figure 16). Figure 16: R value change at the (1000, 1000) pixel during the injection of the sediment mixture. Figure 15: Predicted suspended sediment concentration of (**a**) vertical distribution, and (**b**) horizontal distribution. To address such limitations, it is necessary to determine the conditions for river sediment imaging, such as appropriate camera exposure information and ISO values, through additional experiments; or to conduct further analysis on HSB (that is the combination of the hue, saturation, and brightness that is required to analyze the additional colors of light), or analysis on YCbCr models that separate the brightness component (Y) and color difference information (Cb and Cr) from the RGB colors for expression. In the case of the proposed method for estimating the SSC from color changes in images, it can be concluded that additional experiments and research are required for its actual application to rivers. First, while the experiment that was performed in this study used red clay that causes relatively clear color changes, additional experiments will be required for: (1) the suspended sediment condition that causes relatively small color changes in water (for example, sand); (2) the condition that considers particles with various particle size distributions at the same time; and (3) the condition without a constant flow. Considering the time difference between the turbidity and concentration measurement results and the images that were captured at the same time, image capturing in the direction that is perpendicular to the flow appears to be better than image capturing in the direction that is opposite to the flow.