Development of the Methodology for Pipe Burst Detection in Multi-Regional Water Supply Networks Using Sensor Network Maps and Deep Neural Networks

Hyeong-Suk Kim, Dooyong Choi, Do-Guen Yoo () and Kyoung-Pil Kim ()
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Hyeong-Suk Kim: Department of Civil Engineering, The University of Suwon, Hwaseong-si 18323, Republic of Korea
Dooyong Choi: Water & Wastewater Research Center, K-Water Institute, K-Water, Deajeon 34350, Republic of Korea
Do-Guen Yoo: Department of Civil Engineering, The University of Suwon, Hwaseong-si 18323, Republic of Korea
Kyoung-Pil Kim: Water & Wastewater Research Center, K-Water Institute, K-Water, Deajeon 34350, Republic of Korea

Sustainability, 2022, vol. 14, issue 22, 1-18

Abstract: Multi-regional waterworks are large-scale facilities for supplying tap water to the public and industrial parks, and interruptions in the water supply due to leaks result in massive social and economic damages. Accordingly, real-time, around-the-clock accident monitoring is necessary to minimize secondary damage. In the present study, a section of a large-scale waterworks transmission mains system with frequent changes in its physical boundaries was defined for sensor network map-based deep learning input and output. A deep neural network (DNN)-based pressure prediction model, able to detect pipe burst accidents in real-time using short-term data collected over periods within 1 month, was developed. A sensor network map refers to a sensor-based hierarchy diagram, which is expressed using a hydraulically divided area. A hydraulically independent area can be determined using known value information (e.g., the known flow, pressure, and total head) in a complex water supply system. The input data used for the deep learning model training were: the water levels measured at 1 min intervals, flow rates, ambient pressure, pump operation state, and electric valve opening data. To verify the developed methodology, two sets of real-world data from past burst accidents in different multi-regional waterworks systems were used. The results showed that the difference between the pressure as measured by pressure meters and an estimated pressure was extremely small before an accident, and that the difference would reach a maximum at the time point when an accident occurs. It was confirmed that an approximate estimation of an accident occurrence and accident location could be estimated based on predicted pressure meter data. The developed methodology predicts a mutual influence between pressure meters and, therefore, has the advantage of not requiring past data covering long time periods. The proposed methodology can be applied immediately and used in currently operational large-scale water transmission main systems.

Keywords: sensor network maps; deep neural networks; pipe burst detection; multi-regional water supply networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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