Smart City Gnosys
Smart city article details
| Title | Securing Drinking Water Supply In Smart Cities: An Early Warning System Based On Online Sensor Network And Machine Learning |
|---|---|
| ID_Doc | 47737 |
| Authors | Lu H.; Ding A.; Zheng Y.; Jiang J.; Zhang J.; Zhang Z.; Xu P.; Zhao X.; Quan F.; Gao C.; Jiang S.; Xiong R.; Men Y.; Shi L. |
| Year | 2023 |
| Published | Aqua Water Infrastructure, Ecosystems and Society, 72, 5 |
| DOI | http://dx.doi.org/10.2166/aqua.2023.007 |
| Abstract | To enhance the quality of life and ensure sustainability in crowded cities, safe management of drinking water using cutting-edge technologies is a priority. This study developed an intelligent early warning system (EWS) for alarming and controlling risks from bacteria and disinfection byproducts in a drinking water distribution system (DWDS), named BARCS (Bacterial Risk Controlling System). BARCS adopts an artificial intelligence (AI) approach to data-driven prediction and considers total chlorine (TCl) concentration as the pivot indicator for risk identification and control. First, the machine learning-based AI model in BARCS can provide a reliable prediction of TCl concentration in a DWDS, with an average R2 of 0.64 for the validation set, while offering great flexibility for BARCS to adapt to various conditions. Second, TCl concentration was proven to be a good indicator of bacterial risk in a DWDS, as well as a cost-effective surrogate variable to assess disinfection byproduct risk. Third, the robustness analysis demonstrates that with state-of-the-art water quality monitoring technologies, online implementation of BARCS in real-world settings is feasible. Overall, BARCS represents a promising solution to the safe management of drinking water in future smart cities. © 2023 The Author(s). |
| Author Keywords | bacterial risk; disinfection byproducts; drinking water safety; early warning system; machine learning; smart city |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
21739  | 0.884 | Meenalochani M.; Hariharan T.; Kumar S.V. | Edge Based Device Using Machine Learning For Water Quality Management In A Smart Campus | 8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024 - Proceedings (2024) |
| 19653 | 0.875 | Dey A.; Islam M.K.; Dhar S. | Development Of An Iot-Based Pipe Water Quality Monitoring And Control System For Smart City | 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024 (2024) |
| 7860 | 0.859 | Hemdan E.E.-D.; Essa Y.M.; Shouman M.; El-Sayed A.; Moustafa A.N. | An Efficient Iot Based Smart Water Quality Monitoring System | Multimedia Tools and Applications, 82, 19 (2023) |
| 6718 | 0.858 | Judeson Antony Kovilpillai J.; Sulaiman S.M.; Mir M.H.; Jayanthy S.; Pragya; Rajkumar N. | Advancing Water Quality Monitoring In Smart Cities Using Machine Learning Techniques | 2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024 (2024) |
| 25072 | 0.858 | Rezk N.G.; Alshathri S.; Sayed A.; El-Din Hemdan E. | Ewais: An Ensemble Learning And Explainable Ai Approach For Water Quality Classification Toward Iot-Enabled Systems | Processes, 12, 12 (2024) |
| 3760 | 0.853 | Bourelly C.; Bria A.; Ferrigno L.; Gerevini L.; Marrocco C.; Molinara M.; Cerro G.; Cicalini M.; Ria A. | A Preliminary Solution For Anomaly Detection In Water Quality Monitoring | Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020 (2020) |
| 11192 | 0.853 | Mishra S.; Anithakumari T.; Jain O. | Automated Detection Of Water Quality In Smart Cities Using Various Sampling Techniques | Sustainable Farming through Machine Learning Enhancing: Productivity and Efficiency (2024) |
| 61995 | 0.852 | Martinez Paz E.F.; Tobias M.; Escobar E.; Raskin L.; Roberts E.F.S.; Wigginton K.R.; Kerkez B. | Wireless Sensors For Measuring Drinking Water Quality In Building Plumbing: Deployments And Insights From Continuous And Intermittent Water Supply Systems | ACS ES and T Engineering, 2, 3 (2022) |