Artificial intelligence approach to predicting river water quality: a review

Abstract

Precise prediction of the water quality time series may provide directions for early warning of water pollution and help policymakers to manage water resources more effectively. This prediction may reveal the proclivity of the characteristic water quality according to the most recent water quality, shifting, and transformation rule of the pollutant in the watershed. The predictive capability of traditional models is constrained due to variability, complexity, uncertainty, inaccuracy, non-stationary, and the non-linear interactions of the water quality parameters. Since the middle of the 20th century, Artificial Intelligence (AI) approaches have been found efficient in bridging gaps, simulating, complementing deficiencies, and improving the precision of the predictive models in terms of multiple evaluation measures for better planning, design, deployment, and handling of multiple engineering systems. This article discusses the state-of-the-art implementation of AI in water quality prediction, the type of AI approaches, the techniques adopted include the knowledge-based system as well as literature and their potential future implementation in water quality modelling and prediction. The study also discusses and presents several possibilities for future research.