Advanced Machine Learning Techniques for Water Quality Prediction and Management: A Comprehensive Review

Abstract

The incorporation of IoT, machine learning, and geospatial technologies has rapidized pace in data-driven approaches in water quality monitoring. The approach calls for data-driven methods in water quality assessment that would ensure such a process makes it not only accurate but cost-efficient and within the constraints of applied growing environmental challenges. The IoT sensors allow for real-time data generation, and the machine learning models, such as support vector machines, neural networks, and regression techniques, have changed the index of water quality prediction and analysis. Applications of GIS provide spatial visualization and management of water resources. This collection of papers constitutes the constraints in classical measuring techniques, advanced solutions through automation technologies based on sensor powers, and hybrid algorithms. However, the integration of these technologies solves the complexities associated with water quality measurement apart from having a basis for the supportive suggestions for sustainable water management to provide actionable insights for decision-makers. Hence, this review underlines the potential of such future integration of IoT, AI, and GIS technologies to revolutionize the monitoring of water quality, ensuring clean water through global environmental changes.