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

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. 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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems.
• 2 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.
• 3 Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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