Real-time water quality monitoring using AI-enabled sensors: Detection of contaminants and UV disinfection analysis in smart urban water systems

Abstract

This study introduces a novel method for assessing water quality, employing a cutting-edge sensor system integrated with artificial intelligence (AI) technologies. Addressing the global challenge of water scarcity and pollution, the research focuses on the innovative use of spectroscopic analysis for real-time water quality monitoring. The study evaluates the effectiveness of this system in distinguishing between clean, contaminated, and UV-disinfected water samples, highlighting its precision in detecting variations in water quality. Central to the research is the deployment of advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, to process and classify spectral data. These models demonstrate remarkable accuracy in real-time classification, underscoring the synergy between AI and environmental science in addressing critical public health issues. Significantly, the study showcases the potential of UV disinfection in water treatment, as evidenced by the spectral changes observed in disinfected water samples. This aspect of the research emphasizes the role of spectral analysis in verifying the efficacy of water treatment processes. Overall, this study paves the way for more sophisticated and accessible water quality monitoring systems, offering a promising solution to one of the most pressing environmental challenges. The integration of AI and spectral analysis in this research offers a breakthrough in ensuring a safe water supply and effective water resource management. This study utilizes advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, for water quality assessment. These models process and classify spectral data with high precision, highlighting variations in water quality.