Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation. (1st February 2023)

• Record Type: Journal Article
• Authors: Yan, Tao; Zhou, Annan; Shen, Shui-Long
• Is Part Of: Environmental pollution. Volume 318(2023)
• Publication Date: 2023-02-01
• Subjects: Water quality prediction -- Trend analysis -- Key pollution indicators -- Machine learning -- Bayesian optimisation; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.120870
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Water quality assessment is critical to better recognise the importance of water in human society. In this study, a new framework to predict long-term water quality is proposed by using Bayesian-optimised machine learning methods and key pollution indicators collected from monitoring stations in the Pearl River Estuary, Guangdong, China. The optimised stacked generalisation (SG-op) model achieved the best performance with the highest accuracy (0.992) and Kappa coefficient (0.987). Feature importance of the prediction model was consistent with key pollution indicators. The Spearman rank correlation coefficient was used to determine the significance level of the variation trends of different pollution indicators. The results show that the total phosphorus (TOP), dissolved oxygen (DO), chemical oxygen demand (COD), and petroleum (PET) among the key pollution indicators were on an upward trend in the study area. This framework can be applied to efficiently predict future water quality and to provide technical support for emergency pollution control. Graphical abstract: Image 1 Highlights: Developed a framework to predict water quality levels and reveal pollution trends. Model integrates machine learning methods with Bayesian optimisation algorithm. Consistency between feature importance and key pollution indicators was analysed. Provided technical support for emergency pollution control and water quality management.