Using machine learning to reveal seasonal nutrient dynamics and their impact on chlorophyll-a levels in lake ecosystems: A focus on nitrogen and phosphorus

Chlorophyll-a (Chl-a) is a pivotal indicator of lake eutrophication. Studies examining nutrients limiting lake eutrophication at large scales have traditionally focused on summer and autumn, potentially limiting the applicability of their findings. This study encompasses 86 state-controlled points in the Eastern China Basin, spanning data collected from January 2020 to July 2023. Furthermore, we focus on the application of three machine-learning models (i.e., eXtreme Gradient Boosting, Support Vector Machines, and Naive Bayes Classifier) to analyze the seasonal nutrient dynamics in lake ecosystems. We categorized the monitoring data by season to eliminate outliers and employed adaptive synthetic sampling to address data imbalance issues. The results reveal that the direct correlations between total nitrogen (TN), total phosphorus (TP), and TP in conjunction with turbidity and Chl-a are broadly weak, possibly because of geographic variations, nutrient lag effects on algae, and differences in algal community composition. However, probabilistic analyses revealed that as TP or TN levels transitioned from oligo-mesotrophic (O) to eutrophic (E), TP exhibited a greater influence on the variation in Chl-a status than TN during spring and winter (p < 0.05). Conversely, the effects of TP and TN on Chl-a (O-E) were comparable during summer and autumn. Seasonal variations in TN and TP thresholds derived from XGBoost modeling for O and E states of Chl-a suggest the need for stricter control measures during periods of high-nutrient levels and cost-effective management strategies to employ during low-nutrient periods. These findings should enhance our understanding of trophic shifts in lakes and provide a foundation for optimizing eutrophication management strategies across all seasons.