Algorithm for monitoring water quality parameters in optical systems based on artificial intelligence data mining

• Published in: Scientific reports Vol. 14; no. 1; pp. 28142 - 10
• Main Authors: Su, Jie, Xu, Weining, Lin, Ziyu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 704/172; 704/172/169; 704/172/4081; Algorithms; Artificial intelligence; Data mining; Environmental monitoring; Experimental research; Humanities and Social Sciences; multidisciplinary; Optical systems for data mining; Pollution monitoring; Science; Science (multidisciplinary); Visible spectrum technology; Water monitoring; Water pollution; Water quality; Water quality management; Water quality monitoring; Visible spectrum technology; Optical systems for data mining; Water pollution; Water quality monitoring
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-76700-7

Due to the increasingly serious water environment pollution, the difficulty of Water Quality Monitoring (abbreviated as WQM for convenience) is also constantly increasing, which puts forward more requirements for the capabilities of various aspects of WQM systems. However, the current WQM method has drawbacks such as slow speed, long monitoring time, complex operation, poor stability, and the inability to obtain accurate information on water pollution in the first time, as well as the generation of toxic and harmful secondary pollutants after some measurement parameters are tested. To address these issues and ensure water quality safety, this paper investigated the algorithm for monitoring water quality parameters using artificial intelligence data mining optical systems. This article applied an artificial intelligence data mining system to detect water quality and designed various system through this method to improve system performance. To verify the actual effectiveness of artificial intelligence data mining systems, this article selected 10 water plants as experimental research subjects and compared the differences between traditional WQM methods and WQM methods based on artificial intelligence data mining systems in terms of WQM time, accuracy, sensitivity, and protective performance. The experimental results showed that the optical system based on artificial intelligence data mining took an average of 2.7 days in WQM and the average accuracy was 85.95%. The average sensitivity value was 84.19% and the average protective score was 8.46 points. This indicated that artificial intelligence data mining optical technology had vital significance and value for WQM.