Recent increases in rainfall duration and intensity due to climate change have heightened the importance of levee stability. However, previous studies on levee failure, primarily caused by seepage and overtopping, have mostly examined these causes independently owing to their distinct characteristics. In this study, we conducted lateral overtopping failure experiments under seepage conditions that closely resembled those in experiments conducted in previous studies. Seepage was monitored using water pressure sensors and a distributed optical fiber cable that provided continuous heat for temperature monitoring in the levee. Τhe analysis of levee failure due to lateral overtopping, in the presence of seepage, was conducted using image analysis with digitization techniques and machine learning-based color segmentation techniques on the protected lowland side of the levee, targeting the same area. The results revealed that levee failure occurred more than twice as fast in experiments where seepage conditions were considered compared to the experiments where they were not. Thus, levees weakened by seepage are more vulnerable to overtopping and breaching. Consequently, employing a comprehensive approach that integrates various monitoring and analysis methods for assessing levee stability is preferable to relying on a single method alone.

The increasing importance of water quality management in water supply systems requires the development of efficient methodologies for the early detection of water quality incidents related to the detection of anomalies in water quality parameters. Research aims to analyze real-time water quality data (pH, turbidity, electrical conductivity, temperature, and chlorine), perform anomaly detection across parameters, and conduct a comprehensive investigation of water quality incidents that correlate with detected anomalies in water supply systems. This study can contribute to the development of an early detection and response system related to water quality incidents in water supply systems. Future work will focus on enhancing the application of systems for early detection of water quality incidents by expanding the data, developing anomaly detection methods by applying machine learning techniques, and figuring out the correlations between anomalies and water quality incidents.

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.

An effective understanding of sediment deposition and erosion in river basins, particularly floodplains, is critical for modeling geomorphic evolution, managing flood risks, and maintaining ecological integrity. However, most related studies have been limited to hydraulic or hydrodynamic modeling approaches. Therefore, this study integrated Sentinel-1 differential interferometric synthetic aperture radar (DInSAR) coherence, Sentinel-2 normalized difference vegetation index, and soil surface moisture index data with one-dimensional hydraulic modeling to assess flood-induced sediment deposition and erosion in the Gamcheon River basin under non-flood, short flood, and long flood scenarios. The DInSAR deformation analysis revealed a clear pattern of upstream erosion and downstream deposition during flood events, indicating a total depositional uplift of 0.33 m during the long flood scenario but dominant erosion with a total measured surface lowering of −2.03 m during the non-flood scenario. These results were highly consistent with the predictions from the hydraulic model and supported by the hysteresis curves for in situ suspended sediment concentration. The findings of this study demonstrate the effectiveness of the proposed integrated approach for quantifying floodplain sediment dynamics, offering particular application value in data-scarce or inaccessible floodplains. Furthermore, the proposed approach provides practical insights into sediment management, flood risk assessment, and ecosystem restoration efforts.