This study evaluated the applicability of existing sediment yield and transport estimation models developed using data mining classification and prediction techniques and validated them. Field surveys were conducted by using an acoustic Doppler current profiler and laser in situ scattering and transmission at measuring points in the main stream of the Nakdong River located where the tributaries of the Geumho, Hwang, and Nam Rivers join. Surveys yielded estimations of water velocity, discharge, and suspended sediment concentrations were measured. In contrast with models based on the general watershed characteristics factors, some models based on hydraulic explanatory flow variables demonstrated an excellent predictability. This is because the selected submodels for validation, which provided excellent prediction results, were based on a large number of calibration data. It indicates that a sufficient number of reliable data is required in developing a sediment yield estimation model using data mining. For practical applications of data mining to extant sediment yield estimation models, comprehensive considerations are required, including the purpose and background of model development, and data range. Furthermore, the existing models should be periodically updated with the consideration of temporal and spatial lumping problems.

Sediment measurement data are essential for sediment transport analysis and therefore highly important in overall river planning. Extant sediment measurement methods consume considerable manpower and time and are limited by factors including economic reasons and worker risks. This study primarily aimed to predict the changes in SSC (Suspended Sediment Concentration) and turbidity by examining the change in color in underwater images. While maintaining a constant flow in a channel, the turbidity and concentration were measured under different SSC. Multiple regression models were developed using turbidity measurement results, and they exhibited high explanatory powers (adjusted R² > 0.91). Furthermore, upon verification using the verification dataset of the experimental results, an excellent predictive power (RMSE ≈ 0.4 NTU) was demonstrated. The model with the highest predictive power, which was inclusive of red and green bands and showed no underlying multicollinearity was used to predict turbidity. Finally, the turbidity and suspended sediment concentration relationship determined from the experimental results was used to estimate the sediment concentration from the color changes in the underwater images. The concentrations that were predicted by the model showed satisfactory results, compared to the measurements (RMSE ≈ 21 ppm). This study indicated the feasibility of continuous SSC monitoring using underwater images as a new measurement method.

For the stable securing of water resources for farming and residential purposes, the embankment, preventing the tidal action and intrusion of seawater into the river, have been constructed in the mouth of many major rivers in Korea. The Seomjin River is one of the major rivers without embankment in their mouth and had been famous for the biodiversity and overall environmental health. However, the brackish water zone in the Seomjin River has been gradually extending upstream due to the factors such as extracting aggregate and dredging in the channel, reclamation of the river mouth, and the depletion of runoff discharge from watershed. The saltwater intrusion extended upstream has resulted in the problems such as abrupt changes in ecosystems and environmental conditions over the brackish water areas. In this study, the extent of salinity intrusion has been estimated with Environmental Fluid Dynamics Code (EFDC) model for the total reach of the Seomjin River with the length of 78 km (from Gurye Gauging Station to Yeosu Tide Station, including the endpoint of the Seomjin River). The antecedent rainfall events has been applied to the established model for calibration and verification of the model. In order to consider the effect of the dam construction and resulting changes in runoff characteristics of watershed, the flow duration analyses have been conducted for the cases of before and after the construction of Juam Dam. The results show that the construction of dam and conveying water over the basins result in the depletion of the streamflow, and the depletion of discharge from watershed extend the range of salinity intrusion about 1 km upstream.

The amount of suspended sediment transported from rivers to the ocean fluctuates over time, with a substantial increase occurring during storm events. This surge in sediment poses numerous challenges to coastal areas, highlighting the importance of accurately assessing the sediment load to address these issues. In this study, we developed and experimentally verified a novel method for suspended-sediment-discharge quantification in estuaries and coasts using underwater imaging. Specifically, red clay samples with different particle sizes were introduced into separate tanks containing clean water. After adequate mixing, the concentration, particle size, turbidity, and water quality were measured and analyzed using LISST-200x and EXO2 Multiparameter Sonde sensors. To maintain constant lighting conditions, a camera box was created for filming. Based on the experimental results, a turbidity–concentration relationship formula was derived. The proposed regression equation revealed that the relationship between the turbidity and estimated suspended-sediment concentration was significantly affected by the particle size, and the prediction results were underestimated under high-concentration conditions. Using blue, green, and gray band values, a multiple regression model for estimating suspended-sediment concentrations was developed; its predictions were better than those obtained from the turbidity–concentration relationship. Following efficiency improvements through additional approaches considering underwater-image filming conditions and characteristics of actual streams, estuaries, and coasts, this method could be developed into an easily usable technique for sediment-discharge estimation, helping address sediment-related issues in estuaries and coastal regions.

We present a procedure to systematically estimate the form friction factor through the analysis of spatial data obtained by acoustic Doppler current profiler. The form friction factor is related to the geometric characteristics of the bedform. The zero-crossing method was used to determine the geometric parameters of the bedform, which were then utilized to construct the probability density function for Chezy coefficients. Subsequently, Chezy coefficients were generated from the probability density function and assigned to each grid cell of a numerical model. To evaluate the performance of the presented friction factor estimation method, it was applied to the numerical simulation for the Nakdong River in South Korea. Sensitivity tests were performed to address the potential influence of random extraction from the probability density function, filtering length, and different form predictors during the estimation process. The proposed method showed more robust and accurate results compared to those with Manning’s n value.