Development of a Water Quality Event Detection and Diagnosis Framework in Drinking Water Distribution Systems with Structured and Unstructured Data Integration

Taewook Kim, Donghwi Jung (), Do Guen Yoo, Seunghyeok Hong, Sanghoon Jun and Joong Hoon Kim
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Taewook Kim: Department of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea
Donghwi Jung: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea
Do Guen Yoo: Department of Civil Engineering, The University of Suwon, Hwaseong-si 18323, Republic of Korea
Seunghyeok Hong: Division of Data Science, The University of Suwon, Hwaseong-si 18323, Republic of Korea
Sanghoon Jun: Hyper-Converged Forensic Research Center for Infrastructure, Korea University, Seoul 02841, Republic of Korea
Joong Hoon Kim: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea

Energies, 2022, vol. 15, issue 24, 1-18

Abstract: Recently, various detection approaches that identify anomalous events (e.g., discoloration, contamination) by analyzing data collected from smart meters (so-called structured data) have been developed for many water distribution systems (WDSs). However, although some of them have showed promising results, meters often fail to collect/transmit the data (i.e., missing data) thus meaning that these methods may frequently not work for anomaly identification. Thus, the clear next step is to combine structured data with another type of data, unstructured data, that has no structural format (e.g., textual content, images, and colors) and can often be expressed through various social media platforms. However, no previous work has been carried out in this regard. This study proposes a framework that combines structured and unstructured data to identify WDS water quality events by collecting turbidity data (structured data) and text data uploaded to social networking services (SNSs) (unstructured data). In the proposed framework, water quality events are identified by applying data-driven detection tools for the structured data and cosine similarity for the unstructured data. The results indicate that structured data-driven tools successfully detect accidents with large magnitudes but fail to detect small failures. When the proposed framework is used, those undetected accidents are successfully identified. Thus, combining structured and unstructured data is necessary to maximize WDS water quality event detection.

Keywords: anomaly detection; water quality event; framework; structured and unstructured data integration; water distribution system; water quality (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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