Artificial intelligence based detection and control strategies for river water pollution: A comprehensive review

Abstract

Water quality (WQ) is a metric for assessing the overall health and safety of water bodies like a river. Owing to the habitation of anthropogenic habitation around its basin, the rivers can become one of the most contaminated water sources globally. The solutions to prevent and remit the impact of river water pollution faces many challenges, one of these entails the management of nonlinear, nonstationary water related dataset. This paper provides a detailed overview of Artificial Intelligence (AI) based techniques and algorithms, highlighting their practical applications in the critical domain of river water pollution diction and control. This review shows models for river WQ simulation from 2019 to 2024, in which over 110 research articles from various databases are analyzed. Key advancements in Machine Learning (ML) and Deep Learning (DL) technologies, including Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Random Forest (RF), are highlighted. Besides that, the amalgamation of Internet of Things (IoT) technologies is tested, showing their role in enhancing real-time monitoring and predictive capabilities through continuous data collection and advanced ML/DL models. This review addresses critical challenges and identifies emerging opportunities for future research by showcasing the application of ML, DL, and IoT innovations in surface WQ modeling. It highlights the potential of leveraging advanced technologies to form strengthen solutions for sustainable water resource management and the protection of vital aquatic ecosystems.