EDGE-AI WITH EXPLAINABLE TINYML FOR REAL-TIME WATER QUALITY MONITORING AND PREDICTIVE ANALYTICS

Abstract

Background The security and safety of water resources is essential to human health, agriculture, and ecosystem. Conventional water quality monitoring systems are normally based on centralized cloud infrastructures, which cause delays, excessive consumption of energy, and narrow implementation in remote or poorly resourced regions. Objective This paper suggests an explicable TinyML-based edge architecture to monitor water quality in real-time and predictive analytics. Machine learning model applications can be deployed either on the edge devices directly, which allows sensor data to be instantly processed and inferred without ongoing reliance on the cloud. Incorporating the use of explainable artificial intelligence (XAI), the system will determine which parameters the most significantly impact water quality, including pH, turbidity, and dissolved oxygen, and give transparent information to the user and the decision-makers. Materials and Methods The framework helps to monitor outliers and contamination early, and send real-time warning to avert possible risks. The low-latency performance, energy efficiency, and high predictive accuracy of the system are experimentally assessed and are superior to the traditional cloud-based methods. Results The primary innovation is the integrated focus on TinyML plus edge computing and XAI that have never been additionally combined in the literature to predict the water quality. Conclusion This solution presents a scalable, interpretable, and sustainable solution to smart water management, and especially in isolated or underserved areas, this solution will bridge the gap between IoT sensing, AI prediction and practical deployment.