Real-time water quality monitoring using AI-enabled sensors: Detection of contaminants and UV disinfection analysis in smart urban water systems
Yazarlar (1)
Doç. Dr. Yeliz DURGUN
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Journal of King Saud University - Science |
| Dergi ISSN | 1018-3647 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 08-2024 |
| Cilt No | 36 |
| Sayı | 9 |
| DOI Numarası | 10.1016/j.jksus.2024.103409 |
| Makale Linki | http://dx.doi.org/10.1016/j.jksus.2024.103409 |
| Özet |
| 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 … |
| Anahtar Kelimeler |
| Water quality assessment | Multispectral spectroscopic sensors | Artificial intelligence in environmental | monitoring | Machine learning in water safety |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 22 |
| Google Scholar | 49 |