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A review of the application of machine learning in water quality evaluation

2022; Elsevier BV; Volume: 1; Issue: 2 Linguagem: Inglês

10.1016/j.eehl.2022.06.001

2772-9850

Mengyuan Zhu, Jiawei Wang, Yang Xiao, Yu Zhang, Linyu Zhang, Hongqiang Ren, Bing Wu, Lin Ye,

Water Quality Monitoring and Analysis

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.