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. Despite advancements in sensor allowing for the measurement of various parameters, sensors still cannot directly measure T-N, necessitating surrogate regression methods. Therefore, we conducted T-N predictions using a water quality dataset collected from 2018 to 2022 at 157 observatories within the Nakdong River basin. To account for the water quality characteristics of each location, we employed a clustering technique to divide the basin and compared a Gaussian mixture model with K-means clustering. Moreover, optimal regressor for each cluster was selected by comparing multiple linear regression (MLR), random forest, and XGBoost. The results showed that forming four clusters via K-means clustering was the most suitable approach and MLR was reasonably accurate for all clusters. Subsequently, recursive feature elimination cross-validation was used to identify suitable parameters for T-N prediction, thus leading to the construction of high-accuracy T-N prediction models. Clustering was useful not only for improving the regressors but also for spatially analyzing the water quality characteristics of the Nakdong River. The MLR model can reveal causal relationships and thus is useful for decision-making. The results of this study revealed that the combination of a simple linear regression model and clustering method can be applied to a wide watershed. The clustering-based regression model showed potential for accurately predicting T-N at the basin level and is expected to contribute to nationwide water quality management through future applications in various fields.

Comparison of Optimal Machine Learning Algorithms for Early Detection of Unknown Hazardous Chemicals in Rivers Using Sensor Monitoring Data.

Water environment pollution due to chemical spills occurs constantly worldwide. When a chemical accident occurs, a quick initial response is most important. In previous studies, samples collected from chemical accident sites were subjected to laboratory-based precise analysis or predictive research through modeling. These results can be used to formulate appropriate responses in the event of chemical accidents; however, there are limitations to this process. For the initial response, it is important to quickly acquire information on chemicals leaked from the site. In this study, pH and electrical conductivity (EC), which are easy to measure in the field, were applied. In addition, 13 chemical substances were selected, and pH and EC data for each were established according to concentration change. The obtained data were applied to machine learning algorithms, including decision trees, random forests, gradient boosting, and XGBoost (XGB), to determine the chemical substances present. Through performance evaluation, the boosting method was found to be sufficient, and XGB was the most suitable algorithm for chemical substance detection.

Experimental Study on the Detection of Hazardous Chemicals Using Alternative Sensors in the Water Environment.

Chemical accidents in rivers may be triggered by natural or anthropogenic causes and refer to the flow of large quantities of hazardous chemicals into rivers. In South Korea, domestic water is sourced from large rivers, such as the Nakdong River. However, owing to rapid industrialization, industrial facilities have become heavily concentrated in the middle and upper reaches of the Nakdong River. Therefore, severe problems could arise if harmful chemicals are leaked from industrial facilities into the river, and this contaminated river water is supplied to cities. Quantitative evaluation based on instrumental analysis during chemical accidents and prediction research based on modeling is actively being conducted however, research on the initial response is insufficient. Therefore, in this study, the variations in pH and EC were analyzed according to their chemical concentrations for seven chemicals. These seven chemicals are designated accident-preparedness substances that frequently cause chemical spills in South Korea. Additionally, we evaluated the possibility of identifying unknown substances by comparing the variations in pH and EC and statistics while diluting unknown substances. Thus, the potential of pH and EC as alternative indicators for detecting and identifying chemicals was evaluated in this study. NaF, NH4HF2, NaCN, and NH4OH were classified by comparing their spatial distributions in a pH-EC relation curve. However, H2SO4, HCl, and SOCl2 showed similar spatial distributions in the pH-EC curves and were difficult to identify. The results of this study provide information for chemical detection and identification using alternative sensors that permit easy and rapid field measurements in the event of a chemical spill and could be used as preliminary data for rapidly responding to accidents.