Artificial intelligence in water quality monitoring: A review of water quality assessment applications

Abstract

Artificial intelligence (AI) has become a useful tool in numerous domains, including environmental science. This review explores the application of machine learning and deep learning, as AI technologies, applied in calculating and modelling water quality indexes (WQIs) and water quality classification. WQIs are used to assess the overall status of water bodies and compliance with environmental regulations. Given a large amount of monitoring data, traditional methods for calculating WQIs can be labour-intensive and subject to human error. AI offers a compelling alternative, with the potential to enhance accuracy, reduce time, and provide insights into complex environmental data. This paper examines recent progress in applying AI to water quality assessment through WQIs, including the creation of predictive models that incorporate diverse water quality parameters and the implementation of AI in real-time monitoring systems. The challenges of deploying AI, such as data availability, model transparency, and system integration, are also discussed. Through a detailed analysis of recent studies and practical implementations, this review analyses the potential of AI to contribute to water quality management and suggests directions for future research.