Water Quality Monitoring in the Era of Big Data: Sensors, AI, and Integrated Frameworks

Abstract

Water quality monitoring is rapidly evolving from traditional periodic sampling towards continuous, data-intensive, and intelligent systems. This review examines the current trends in the field of water quality monitoring within the context of big data, and specifically focuses on the intersection of sensor technologies, artificial intelligence (AI), and integrated monitoring systems. Originally, the article examines the current sensing methods, such as electrochemical sensors, optical and spectroscopic devices, biosensors, IoT-enabled networks, remote sensing, and mobile platforms, with an emphasis on their increased monitoring performance and their persistent shortcomings in the areas of fouling, calibration, selectivity, and field performance. Second, it addresses the distinguishing features of water quality big data and highlights the issues of heterogeneity, velocity, uncertainty, preprocessing, data fusion, interoperability, and governance. Third, the paper assesses the use of AI, with special focus on machine learning, deep learning, and water quality assessment, prediction, anomaly detection, and decision support, and critically discusses concerns related to transferability, interpretability, reproducibility, and quantifying uncertainty. Lastly, the article brings together the synthesis of how the intertwining of frameworks of sensing, data infrastructure, analytics, and management platforms can help to transform fragmented observations of water quality into proactive and adaptive control. This review uniquely adopts a systems-level perspective, treating sensors, big-data management, and AI as integrated layers of next-generation environmental intelligence.