## 5 Conclusions  
This paper proposes the use of underwater-image color changes to quantitatively estimate suspended-sediment discharge, which is essential to solve various issues related to sedimentation on coasts or in rivers or estuaries. In an experiment, 100 g of red clay samples that were classified into five different types based on particle size (200, 325, 700, 1000, and 2000 mesh) were introduced into water tanks with dimensions of 1.2 \(\times\) 1.8 m. Every second, the concentration, turbidity, suspended-sediment particle size, underwater images, and water quality factors were recorded using the LISST-200x, EXO2 Multiparameter Sonde, and a Go-Pro camera. Of the 2958 data points, 968 with low reliability or those measured during the introduction of the suspended sediment were excluded from the analysis, and 489 points were used for the validation of the suspended-sediment concentration estimation model. For the underwater images, an underwater light source installed within the camera box supplied constant light during image recording. The underwater images of the 325, 700, and 2000 mesh samples were analyzed and used for subsequent model development.  
Because turbidity is typically measured according to the scattering rate, the particle size of the suspended sediments was expected to significantly impact the turbidity-concentration relationship. Next, three most commonly used forms of the turbidity-concentration relationship were applied to the experimental results. Among these, the power law form presented the highest correlation (R\({}^{2}\) = 0.66). To identify color changes in the underwater image according to concentration changes based on the supplied pixels from the light source, the RGB and gray band value changes were observed. For all of the bands except red, the values tended to decrease as the concentration increased. Finally, the analyzed band values were used to develop a log-transformed multiple regression model for turbidity and suspended-sediment-concentration prediction. The validation and calibration datasets were utilized to compare the suspended-sediment-concentration predictions with those obtained from the turbidity-concentration relationship and the concentration predicted by the turbidity from the predicted turbidity model. The concentrations obtained from the predicted turbidity model were significantly low in the validation data section under high-concentration conditions (RMSE = 180 mg L\({}^{-1}\)). In contrast, the suspended-sediment concentration that was estimated based on underwater-image color changes was not affected by the particle size, and the prediction was reliable, even at high concentrations (RMSE = 99 mg L\({}^{-1}\)). The method proposed in this paper allows for steady monitoring and could be more efficient and easier to use compared with the existing methods. To ensure practical applicability of the proposed method, establishing a connection with existing measurement methods is crucial. Furthermore, additional experiments and research will contribute to the further development of this method as a practical approach for quantifying suspended-sediment discharge. Such a method will play a valuable role in addressing sediment-related challenges in estuaries and coastal areas.  
Conceptualization, W.K. and S.K.; formal analysis, W.K., K.L. and S.K.; writing--review and editing, W.K. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a National Research Foundation of Korea (NRF) grant that was funded by the Korean government (No. 2021R1C1C101040411).  
Not applicable.  
This study used an experimental infrastructure at the River Experiment Center of the Korea Institute of Civil Engineering and Building Technology.  
The authors declare no conflict of interest.