Analysis of microplastic behaviors in river-type lakes using a quasi-three-dimensional microplastic transport model.

Paldang Lake in South Korea is a river-type lake characterized by weak temperature stratification and a short water residence time. It exhibits complex flow structures resulting from the convergence of two major rivers, the North Han River (NHR) and the South Han River (SHR), and one small river, the Gyeongan Stream. Analyzing the spatio-temporal variations in microplastic (MP) concentration in Paldang Lake is important because of its significance as a water supply source for the metropolitan area.

Development of a basin-scale total nitrogen prediction model by integrating clustering and regression methods.

Nutrient runoff into rivers caused by human activity has led to global eutrophication issues. The Nakdong River in South Korea is currently facing significant challenges related to eutrophication and harmful algal blooms, underscoring the critical importance of managing total nitrogen (T-N) levels. However, traditional methods of indoor analysis, which depend on sampling, are labor-intensive and face limitations in collecting high-frequency data.

Effects of spectral variability due to sediment and bottom characteristics on remote sensing for suspended sediment in shallow rivers.

Advances in remote sensing techniques for water environments have led to acquisition of abundant data on suspended sediment concentration (SSC). However, confounding factors, such as particle sizes, mineral properties, and bottom materials, have not been fully studied, despite their substantial interference with the detection of intrinsic signals of suspended sediments. Therefore, we investigated the spectral variability arising from the sediment and bottom using laboratory and field-scale experiments.

Surrogate prediction of the breakthrough curve of solute transport in rivers using its reach length dependence.

Techniques for predicting the contaminant cloud propagation along a stream are necessary for swift action against contaminant spill accidents in fluvial systems. Due to their low computational cost, one-dimensional solute transport models have conventionally been employed, in which the complex channel characteristics are considered using model parameters. However, the determination of such parameters relies predominantly on optimization techniques based on pre-measured tracer data, which are usually unavailable for unexpected accidents.

Hyperspectral retrievals of suspended sediment using cluster-based machine learning regression in shallow waters.

Remote sensing of suspended sediment in shallow waters is challenging because of the increased optical variability of the water, resulting from the influence of suspended matter in the water column and the heterogeneous bottom properties. To overcome this limitation, in this study, we developed a novel framework called cluster-based machine learning regression for optical variability (CMR-OV), using the Gaussian mixture model (GMM) clustering technique and a random forest regressor (RFR). We evaluated the model using an optically complex dataset from a field-scale experiment.

Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis.

To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predict the spill location and mass of a contaminant source. The TSM model was employed to simulate non-Fickian Breakthrough Curves (BTCs), which entails relevant information of the contaminant source.