An Extensive Survey of IoT and AI-Driven Water Quality Monitoring System

Abstract

Retaining water quality is significant for human welfare, ecological balance, also sustainable water source handling. Rapid industrialization, development of urban area, construction of buildings and agricultural activities have significantly increased water pollution, creating a need for reliable and continuous monitoring systems. Traditional water quality evaluation techniques depend on sample collection and lab sample inspection, that takes more time to be processed, expensive, and not suitable for continuous evaluation. Recent advanced technologies in the Internet of Things (IoT), wireless communication, innovations in sensor technologies, cloud services, and Artificial Intelligence (AI) have allowed the development of smart water quality monitoring technologies capable of continuous data collection, evaluation, and decision-making. These systems continuously monitor key water quality parameters(WQP) like pH, temperature, turbidity, dissolved oxygen (DO), and total dissolved solids (TDS). Furthermore, machine learning techniques using random forest, support vector machine (SVM), XG Boost, and long short-term memory (LSTM) networks were widely used to upgrade the prediction accuracy and detect abnormalities in the data of water quality. This survey presents a systematic analysis of recent IoT- and AI-based water quality monitoring techniques, covering sensing technologies, communication methods, cloud integration, and machine learning approaches. It also discusses existing challenges related to sensor reliability, communication efficiency, data imbalance, and computational requirements in instant time water monitoring system, also the review highlights the possibility of scalable, intelligent, and Water Quality Monitoring System (WQMS) which is environmentally sustainable or effective water resource handling.