Securing drinking water supply in smart cities: an early warning system based on online sensor network and machine learning

Autor: Haiyan Lu, Ao Ding, Yi Zheng, Jiping Jiang, Jingjie Zhang, Zhidong Zhang, Peng Xu, Xue Zhao, Feng Quan, Chuanzi Gao, Shijie Jiang, Rui Xiong, Yunlei Men, Liangsheng Shi
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Aqua, Vol 72, Iss 5, Pp 721-738 (2023)
Druh dokumentu: article
ISSN: 2709-8028
2709-8036
DOI: 10.2166/aqua.2023.007
Popis: 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. HIGHLIGHTS BARCS predicts and regulates bacterial risk in pipelines.; TCl prediction module harvests an average R2 of 0.64 for the validation set.; The relationship between TCl and CFU is quantified.; Online implementation of BARCS in real-world settings is feasible.;
Databáze: Directory of Open Access Journals
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