Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10634 - 10634

Published: Dec. 4, 2024

Accurately predicting the state of surface water quality is crucial for ensuring sustainable use resources and environmental protection. This often requires a focus on range factors affecting quality, such as physical chemical parameters. Tree models, with their flexible tree-like structure strong capability partitioning selecting influential features, offer clear decision-making rules, making them suitable this task. However, an individual decision tree model has limitations cannot fully capture complex relationships between all influencing parameters quality. Therefore, study proposes method combining ensemble models voting algorithms to predict classification. was conducted using five monitoring sites in Qingdao, representing portion many municipal environment stations China, employing single-factor determination stringent standards. The soft algorithm achieved highest accuracy 99.91%, addressed imbalance original categories, reaching Matthews Correlation Coefficient (MCC) 99.88%. In contrast, conventional machine learning algorithms, logistic regression K-nearest neighbors, lower accuracies 75.90% 91.33%, respectively. Additionally, model’s supervision misclassified data demonstrated its good rules. trained also transferred directly at 13 Beijing, where it performed robustly, achieving hard 97.73% MCC 96.81%. countries’ systems, different qualities correspond uses, magnitude related categories; critical can even determine category. are highly capable handling nonlinear important allowing identify exploit interactions parameters, which especially when multiple together there significant motivation develop model-based prediction models.

Language: Английский