Analysis of key factors influencing cyanoHAB intensity levels and evaluation of machine learning predictions

This study quantified the effects of biological and physicochemical factors on the occurrence of cyanobacterial harmful algal blooms (cyanoHABs) at various stages in the Juksan Weir, where cyanoHABs frequently occur due to decreased flow velocity and prolonged residence time caused by artificial structures. The predictive performance of machine learning techniques such as Random Forest (RF), XGBoost (XGB), boosted regression trees (BRT), and a stacking model (RF + XGB) was evaluated. The analysis revealed distinct changes in physicochemical factors and differences in plankton community structure across the stages of cyanoHABs. Water temperature, COD, and DO were identified as common key physicochemical factors, with stage-specific key factors identified for plankton communities. At the low level, Cladocera and Chlorophyceae were dominant, contributing to initial community stabilization. At the moderate level, mixotrophic dinoflagellates (e.g., Cryptomonas ) and Copepoda increased, mediating energy flow redistribution and community changes. At the high level, community reorganization occurred as Protozoa exhibited functional responses to adapt to abundant organic matter and low-oxygen environments. In the machine learning analysis, RF and XGB generally showed high performance, whereas the stacking model (RF + XGB) exhibited the most consistent and accurate predictive power at all levels. All models except BRT were stable at the low level, whereas the stacking model achieved the highest performance at the moderate and high levels. These differences in performance highlight the advantages of utilizing ensemble (stacking) approaches in capturing stage-specific nonlinear interactions. The results of this study demonstrate that a stacking ensemble approach, which incorporates stage-specific key variables, is an effective strategy in achieving accuracy and stability in predicting the intensity levels of cyanoHABs. In the future, this study will provide scientific evidence to support the development of early warning systems for cyanoHABs.