Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea

• Published in: Heliyon 2024-06, Vol.10 (11), p.e31643-e31643, Article e31643
• Main Authors: Jin, Sang-Hyeon, Jargal, Namsrai, Khaing, Thet Thet, Cho, Min Jae, Choi, Hyeji, Ariunbold, Bilguun, Donat, Mnyagatwa Geofrey, Yoo, Haechan, Mamun, Md, An, Kwang-Guk
• Format: Article
• Language: English
• Subjects: Algal chlorophyll; Empirical analysis; Machine learning; Nutrient regimes; Summer monsoon; Temperate reservoir
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e31643

This study analyzed spatiotemporal variation and long-term trends in water quality indicators and trophic state conditions in an Asian temperate reservoir, Juam Reservoir (JR), and developed models that forecast algal chlorophyll (CHL-a) over a period of 30 years, 1993–2022. The analysis revealed that there were longitudinal gradients in water quality indicators along the reservoir, with notable influences from tributaries and seasonal variations in nutrient regimes and suspended solids. The empirical model showed phosphorus was found to be the key determinant of algal biomass, while suspended solids played a significant role in regulating water transparency. The trophic state indices indicated varying levels of trophic status, ranging from mesotrophic to eutrophic. Eutrophic states were particularly observed in zones after the summer monsoons, indicating a heightened risk of algal blooms, which were more prevalent in flood years. The analysis of trophic state index deviation suggested that phosphorus availability strongly influences the reservoir trophic status, with several episodes of non-algal turbidity at each site during Mon. Increases in non-algal turbidity were more prevalent during the monsoon in flood years. This study also highlighted overall long-term trends in certain water quality parameters, albeit with indications of shifting pollution sources towards non-biodegradable organic matter. According to the machine learning tests, a random forest (RF) model strongly predicted CHL-a (R2 = 0.72, p 60 μg/L), compared to all other models. Overall, our research suggests that CHL-a and trophic variation are primarily regulated by the monsoon intensity and predicted well by the machine learning RF model.