Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Internet of Things-Enabled Water Quality Monitoring System. |
| Συγγραφείς: |
M A; Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS)., Rinesh S; Department of Information Technology, V. S. B. College of Engineering Technical Campus., Prajitha C; Department of Electronics and Communication Engineering / Center for Interdisciplinary Research, Karpagam Academy of Higher Education., N G; Department of Computer Science and Engineering, Hindustan College of Engineering and Technology., Kumar APS; School of Social Work, College of Social Sciences and Humanities, Jijiga University; senthilapsk@jju.edu.et. |
| Πηγή: |
Journal of visualized experiments : JoVE [J Vis Exp] 2026 Sep 25 (235). Date of Electronic Publication: 2026 Sep 25. |
| Τύπος έκδοσης: |
Journal Article; Video-Audio Media |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: MYJoVE Corporation Country of Publication: United States NLM ID: 101313252 Publication Model: Electronic Cited Medium: Internet ISSN: 1940-087X (Electronic) Linking ISSN: 1940087X NLM ISO Abbreviation: J Vis Exp Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: [Boston, Mass. : MYJoVE Corporation, 2006]- |
| Ιατρικοί όροι (MeSH): |
Environmental Monitoring*/methods , Environmental Monitoring*/instrumentation , Drinking Water*/analysis , Water Quality* , Internet of Things*, Machine Learning |
| Περίληψη: |
Water quality monitoring protects human health, aquatic habitats, and sustainable water resource management. Manual sampling and laboratory testing are laborious, time-consuming, and unsuited for continuous water quality monitoring. A proof-of-concept IoT-Enabled Water Quality Monitoring System (IoT-WQMS) that uses an IoT-based sensor architecture and machine learning for intelligent water quality evaluation is presented in this work. The framework predicts water potability using pH, hardness, total dissolved solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The publicly available Kaggle Water Potability dataset, comprising 3,276 water samples, was used for experimental assessment. After median-based missing-value imputation, Min-Max normalization, and an 80:20 training-testing split, performance assessment was performed. The suggested prediction model has 98.21% accuracy, 97.86% precision, 98.04% recall, 97.95% F1-score, and a receiver operating characteristic area under the curve (ROC-AUC) of 0.991. The model showed steady and dependable performance with an average prediction latency of 0.18 s per sample and a mean accuracy of 97.94% ± 0.42% after 10-fold cross-validation. Scalability investigation indicated that increasing the dataset size from 20% to 100% increased execution time from 1.8 s to 7.9 s while keeping prediction accuracy above 97.5%. An ESP32 microcontroller, low-cost sensing modules, and cloud-based processing enable an economically viable framework for intelligent water quality assessment, laying the groundwork for future real-time IoT deployment and sustainable environmental monitoring. |
| Substance Nomenclature: |
0 (Drinking Water) |
| Entry Date(s): |
Date Created: 20260927 Date Completed: 20260927 Latest Revision: 20260927 |
| Update Code: |
20260927 |
| DOI: |
10.3791/73066 |
| PMID: |
42801291 |
| Βάση Δεδομένων: |
MEDLINE |