Issue |
MATEC Web Conf.
Volume 246, 2018
2018 International Symposium on Water System Operations (ISWSO 2018)
|
|
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Article Number | 02024 | |
Number of page(s) | 5 | |
Section | Parallel Session I: Water Resources System | |
DOI | https://doi.org/10.1051/matecconf/201824602024 | |
Published online | 07 December 2018 |
Diagnose Urban Drainage Network Problem Based on Internet of Things and Big Data
School of Environment and Municipal Engineering, Qingdao University of Technology, 266033 Qingdao, China
a Corresponding author: Lvmou1@163.com
Urban drainage pipe network is an important foundation project in urban construction. It has a vital impact on urban waterlogging prevention and water pollution. However, in the current practice, there are many hidden problems in the pipe network, and it is difficult to check the pipe network problem, which restricts the understanding of the drainage system problem to a certain extent. In order to solve the two technical problems of drainage network survey and data statistics, the monitoring technology based on Internet of things and big data is adopted in this study. Taking the sponge city pilot area of a coastal city in China as the research area, the monitoring scheme was established and the monitoring data were obtained. Based on more than 6 million monitoring data, the automatic analysis algorithm is applied to analyse the problems of mixed connection of rain and sewage and tidal backwater in the pipeline network. The results show that there are a total of 17 outlets in 160 outlets with problems of rain and sewage mixing. Among them, there are four outlets with regular domestic sewage entering the rainwater pipe network, 7 outlets with irregular sewage entering the rainwater pipe network, and 6 outlets where sewage is smuggled into the rainwater pipe network. In addition, there is a sea tidal backwater phenomenon at one of the coastal rainwater outfalls.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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