## 5 Conclusions  
In this study, an experiment for estimating the SSC from color changes in underwater images was performed to develop a more economical and safer sediment measurement and monitoring method than the conventional methods. At a 32 m point, 828 turbidity data were captured every 2 s using WQC-30, and 1642 concentration data were captured every second using LISST in a 50 m circulating channel. Water mixed with red clay at three different concentrations (25, 50, and 75 gr/L) was injected for inducing color changes in underwater images according to the SSC, and the experiment was then conducted. Changes in the RGB and gray scale values were analyzed by extracting underwater images, and multiple linear regression models for turbidity prediction were developed using the results that were obtained from the images and turbidity measurement results. Using 502 data points, 45 models were developed for the five combinations of RGB and gray scale values at nine pixels that presented high predictive power for turbidity (adjusted R-squared > 0.91), and the models that were developed using the data obtained from the pixels corresponding to the turbidity measurement position particularly showed excellent predictive power. When the models were verified using the remaining 180 verification data points, they showed excellent RMSE values of approximately 4 NTU. Due to some models exhibiting multi-collinearity (VIF > 10), turbidity prediction was finally performed by applying the model incorporating red and green bands. In addition, three turbidity-concentration relationships were suggested using the data that were used for model development and verification. These relationships showed a similar explanatory power (R\({}^{2}\approx 0.7\)); however, they showed less correlations when the average particle size was large before the injection of the red clay mixture or when other suspended matter was mixed in large quantities. Finally, the concentration results that were predicted using the proposed turbidity model and the most commonly used turbidity-concentration relationship that were obtained from the experiment results showed satisfactory prediction results (RMSE \(\approx\) 20 ppm), compared to the measured concentration results.  
For the actual application of the proposed methodology, it is necessary to conduct additional experiments and research considering various experimental conditions and analysis methods. The experimental results, however, have confirmed that the SSC can be monitored from the color changes in underwater images if measurements are continuously performed at fixed points. Monitoring the SSC through underwater image processing is a promising technology, and some of the existing impediments to continuous sediment measurements for river management can be eliminated through additional research and experiments.  
Conceptualization, W.K.; formal analysis, W.K., K.L. and J.K.; writing--review and editing, W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2021R1C1C101040411).  
Not applicable.  
This work utilized an experimental infrastructure at the River Experiment Center of the Korea Institute of Civil Engineering and Building Technology. This research was supported by 'National Research Council of Science & Technology (NST)'-'KOREA INSTITUTE of CIVIL ENGINEERING and BUILDING TECHNOLOGY (KICT)' Postdoctoral Fellowship Program for Young Scientists at KICT in South Korea.  
The authors declare no conflict of interest.