Water Quality Analysis Using Machine Learning - A Review

Abstract

Machine learning has grown in importance as a tool for data analysis, categorization, and prediction as the number of data on water parameters has rapidly increased. Unlike the older models used for studies about water, data-driven models of machine learning can successfully address more challenging problems. Machine learning models and findings have been used in water environment research for the development, monitoring, assessment, simulation, and optimisation of the number of systems for managing and treating water. Also, machine learning (ML) can offer approaches for managing the security of the watershed ecology, improving water quality, and reducing pollution in water. The ML code is applied to predict the quality of water in different water ecosystems, like drinking water, the ocean, sewage, and surface and groundwater. Additionally, we suggest the potential application of machine learning algorithms to aquatic ecosystems. But now that enormous amounts of data are readily available and machine learning techniques have advanced, researchers and water management experts are increasingly using machine learning methods to analyse water quality. To monitor water quality more effectively and accurately, machine learning algorithms analyse complex datasets, spot trends, and generate predictions. Physical, chemical, and biological factors can all be used by machine learning algorithms to analyse water quality. Temperature, pH level, turbidity, and conductivity are examples of physical characteristics, whereas nutrients, heavy metals, and organic molecules are examples of chemical parameters. The existence and diversity of various microbes are examples of biological parameters. However, due to recent developments in machine learning methods and the accessibility of enormous amounts of data, academics and water management experts are increasingly using machine learning techniques for analysing water quality. To more effectively and accurately assess the quality of the water, machine learning algorithms can analyse complex datasets, spot trends, and make predictions.