Smart IoT-enabled Water Quality Management using Generative AI and Super-Resolution GAN for Enhanced Monitoring and Prediction

An essential component of sustainable development is water quality management, which requires accurate monitoring and forecasting capabilities. Conventional methods for assessing water quality often face challenges such as sparse data, sensor inaccuracies, and real-time processing limitations. To address these challenges, this study proposes a Smart IoT-Enabled Water Quality Management System that integrates Super-Resolution Generative Adversarial Networks (SR-GAN) with artificial intelligence (AI) for enhanced monitoring and prediction. The system utilizes IoT -based smart sensors to collect real-time data on temperature, turbidity, oxygen concentration, pH levels, and other water quality parameters.SR-GAN enhances both the temporal and spatial resolution of low-quality sensor data, enabling better feature extraction and more precise information. Generative AI models are then employed for automated decision-making, anomaly detection, and forecasting, effectively identifying potential water pollution threats. Compared to conventional AI and deep learning methods, the proposed framework achieves superior performance in terms of response time efficiency (30% improvement), data quality, and prediction accuracy (98.7%). The integration of edge computing ensures low latency and real-time data processing. This innovative approach significantly enhances water quality monitoring accuracy, facilitating proactive measures and sustainable water resource management.