YoonKyung Cha

Since rivers are adversely affected by harmful algal blooms (HABs), it is necessary to develop countermeasures through analyzing the relationship between environmental variables and HABs. This study focused on analyzing the connections between HABs and environmental variables in the middle reaches of the Nakdong River in South Korea. Partial least squares structural equation modeling (PLS-SEM) was used to identify those relationships. The study developed three different PLS-SEM models to investigate various aspects, including lagged effects of environmental factors, influences from upstream and tributaries, and interactions between genera of HABs. The results of the study revealed that the magnitude of HABs had the strongest relationships with nutrient concentrations, particularly 1 week prior to HABs measurement. Additionally, the magnitude of HABs showed stronger relationships with upstream nutrient concentrations compared with tributaries' nutrient concentrations. Furthermore, the dominant genus of HABs in the study area, Microcystis, showed significant relationships with temperature and nutrient concentrations. However, the study did not find significant relationships between Microcystis and other harmful cyanobacteria genera. The methodological framework provides valuable insight into the management of HABs. It allows for the analysis of multiple aspects of the relationships between environmental factors and HABs, which is crucial for effective water resource management.

Sensor networks enable the collection of high-frequency, large water quality datasets that provide valuable information for managing eutrophication, such as chlorophyll a (Chl-a) concentration. Deep learning models have been successfully applied to derive useful insights from large-scale environmental data. However, sensor data often contain missing values, presenting challenges for applying deep learning models. Therefore, we employed the reverse time attention model with a decay mechanism (RETAIN-D) to simultaneously conduct feature engineering, prediction, and interpretation within a single model structure. Various environmental, hydrological, and meteorological variables were utilized as input features to predict the exceedance of Chl-a criteria. Data were collected from 2018 to 2022 at four monitoring sites along the Geum River, South Korea. RETAIN-D demonstrated strong prediction performance (accuracy = 0.84–0.90, AUC = 0.69–0.91, F-measure = 0.89–0.90 on the test set) across varying Chl-a criteria. Environmental variables were more important than hydrological and meteorological for predicting the exceedance of Chl-a criteria. The contribution of input features to the model prediction was generally higher in more recent time steps when the Chl-a criterion of the target site was applied. These results highlight the effectiveness of RETAIN-D in analyzing high-frequency time series data from sensor networks.