Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications

Abstract

This chapter points out machine learning-Ml-that is set to alter air and water quality monitoring technologies. Traditional monitoring systems usually work satisfactorily; however, there always exists deficiencies regarding data processing and accuracy of response in real time. For this kind of system, ML algorithms would become advantageous for such systems to carry out large-scale environmental data analysis, thereby enhancing predictive capabilities to realize early detection of pollutants. The chapter goes on to explain ML techniques under supervised and unsupervised learning, along with their applications to sensor networks, remote sensing, and data fusion. Case studies on the successful implementation of the ML-driven solution to air and water quality monitoring show the improvement in accuracy and decision-making. Further, the chapter addresses the challenge associated with ML integration in data privacy, algorithmic bias, and the need for robust training datasets.