About Machine Learning Techniques in Water Quality Monitoring

Abstract

Water Quality Monitoring (WQM) faces significant challenges posed by emerging contaminants, non-point source pollutants, and climate change. The continued development of suitable sensing technologies that are likely to produce increasingly large amounts of data, also creates the need for accurate and efficient data analysis and modeling techniques. Artificial Intelligence is set to play a prominent role in performing analyses and predictions based on large datasets. This work hence reviews some leading Machine Learning (ML) approaches and applications in WQM. It also identifies emerging technique applications that can potentially enhance WQM significantly.