Machine learning-based water quality prediction using octennial in-situ Daphnia magna biological early warning system data

Biological early warning system (BEWS) has been globally used for surface water quality monitoring. Despite its extensive use, BEWS has exhibited limitations, including difficulties in biological interpretation and low alarm reproducibility. This study addressed these issues by applying machine learning (ML) models to eight years of in-situ BEWS data for Daphnia magna. Six ML models were adopted to predict contamination alarms from Daphnia behavioral parameters. The light gradient boosting machine model demonstrated the most significant improvement in predicting alarms from Daphnia behaviors. Compared with the traditional BEWS alarm index, the ML model enhanced the precision and recall by 29.50% and 43.41%, respectively. The speed distribution index and swimming speed were significant parameters for predicting water quality warnings. The nonlinear relationships between the monitored Daphnia behaviors and water physicochemical water quality parameters (i.e., flow rate, Chlorophyll-a concentration, water temperature, and conductivity) were identified by ML models for simulating Daphnia behavior based on the water contaminants. These findings suggest that ML models have the potential to establish a robust framework for advancing the predictive capabilities of BEWS, providing a promising avenue for real-time and accurate assessment of water quality. Thereby, it can contribute to more proactive and effective water quality management strategies. Daphnia biological early warning system (BEWS) was utilized for promptly detecting contaminants within rivers. However, there was not a scientific report focusing on machine learning (ML) models aimed at enhancing the accuracy of detecting the pollutants that contributed mostly to observed pollution alerts in rivers. In this study, we successfully developed ML models by utilizing long-term field data to improve the predictability and applicability of BEWS. The results of our modeling efforts provided an interpretative understanding of the intricate relationship between Daphnia behaviors and pollutants, thereby offering valuable insights to the scientific community involved in the research of BEWS.