Environmental Ecology and Human Health: A Review of Data-Driven Approaches for Water Quality Evaluation Using Machine Learning
Abstract
The volume of data related to the aquatic environment has rapidly increased, and machine learning has become an essential tool for data analysis, classification, and prediction. While conventional models used in water-related research tend to be more mechanistic in nature, data-driven machine learning models may be able to solve more complex nonlinear problems efficiently. However, we note that the use of machine learning models and findings has been applied in water environment research to design, monitor, simulate, evaluate, and optimize management systems. ML also contributes to controlling water pollution, improving water quality, and watershed ecosystem security. Machine learning algorithms are well-developed, robust statistical tools that have been applied to many complex problems, including the assessment of different types of water quality in surface water, groundwater, drinking water, sewage, and seawater. we present future uses for machine learning algorithms in aquatic environments.
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