Membrane distillation is a process combining thermal and membrane technology, which uses the vapor pressure difference as driving force because of the temperature difference. Of the MD configurations, VMD can achieve high flux due to the application of vacuum. This paper analyzes operating parameters (vacuum pressure, feed flow rate, and feed concentration) that affect permeate flux in a modified design VMD system vacuum multi-effect membrane distillation (V-MEMD). The study investigated the most suitable operating parameters that would enhance the performance of V-MEMD system at high salinity (1–4 M of NaCl) with a moderate bulk temperature of 60°C. The result of this study showed that vacuum pressure is the most influential factor of the selected, and the effect of temperature polarization is more dominant to permeate flux than concentration polarization. Above this, the high-salinity solution can be treated by V-MEMD system because it does not affect the permeate total dissolved solid and flux. A dominant reduction ratio of vapor pressure was observed even at a low ratio of temperature polarization.

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.

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.

This study employs technology that has many different applications, including flood management, flood level control, and identification of vegetation type by patch size. Recent climate change, characterized by severe droughts and floods, intensifies riparian vegetation growth, demanding accurate environmental data. Traditional methods for analyzing vegetation in rivers involve on-site measurements or estimating the growth phase of the vegetation; however, these methods have limitations. Unmanned aerial vehicles (UAVs) and ground laser scanning, meanwhile, offer cost-effective, versatile solutions. This study uses UAVs to generate 3D riparian vegetation point clouds, employing the alpha shape technique. Performance was evaluated by analyzing the estimated volume results, considering the influence of the alpha radius. Results are most significant with an alpha radius of 0.75. This technology benefits river management by addressing vegetation volume, scale, flood control, and identification of vegetation type.

In this study, using the modified Einstein procedure, data on sediment load were systematically collected and analyzed by year, period, watershed, and main stream and tributaries in South Korean rivers. It was found that the sediment load and concentration differ significantly depending on the region, even in the same area and at the same time, especially in areas with a large minimum-maximum discharge ratio. Therefore, the application of the relationship between discharge and sediment load across all time and space needs to be precise, and data have to be selected separately according to the relational application conditions. In addition, existing sediment transport formulas were compared by applying various goodness-of-fit methods that were well suited to the measured dataset. The results showed that the optimal sediment transport formula varied according to the goodness-of-fit method. Furthermore, while the Ackers and White approach was found to be generally suitable; however, the correlation figures alone were not conclusive. Therefore, a quantitative determination must be made as to how the application of simple fitness methods affects the selection of optimal sediment transport formulas.