Proactive Detection of Wastewater Overflows for Smart Sanitary Sewer Systems: Case Study in South Australia
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 1
Abstract
Sewage systems are built to carry contaminated wastewater from domestic discharge points to collection points for treatment. However, their capacity can be reduced by obstructions, displaced pipe joints, or broken pipes, creating abnormal hydraulic conditions. Wastewater overflows are a potential consequence of these abnormal conditions, which pose a direct threat to the environment and human health. This paper describes a permanent continuous monitoring system of a real sewage network using ultrasonic water level sensors for the purpose of blockage/choke detection, as installed in the suburb of Stonyfell, South Australia. From 62 available data sets collected over 1 year, two distinctive features of growing chokes were identified, including irregular peaks and durations that the water level remains irregularly high in the sewer maintenance holes. An early choke detection method was formulated based on the later feature, which continuously scans the near-real-time data to find time periods containing these abnormal water levels. Application of the methodology showed that the proposed method is effective in detecting possible chokes and overflow events before they occur. In most cases, the warning from the first detection is early enough to allow proactive maintenance attendance to be scheduled by the water utility.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some data and code that support the findings of this study are available from the corresponding author upon reasonable request. These include the codes of the proposed methodology and selected subsets of data from the choke events listed in Table 1, subjected to the Water Utility’s approval. Some data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. These include the water level data sets.
References
Bailey, J., E. Harris, E. Keedwell, S. Djordjevic, and Z. Kapelan. 2016. “Developing decision tree models to create a predictive blockage likelihood model for real-world wastewater networks.” Procedia Eng. 154 (2): 1209–1216. https://doi.org/10.1016/j.proeng.2016.07.433.
Bin Ali, M. T., K. V. Horoshenkov, and S. J. Tait. 2011. “Rapid detection of sewer defects and blockages using acoustic-based instrumentation.” Water Sci. Technol. 64 (8): 1700–1707. https://doi.org/10.2166/wst.2011.183.
Cameron, B., M. McGowan, C. Mitchell, J. Winder, R. Kerr, and M. Zhang. 2017. “Predicting sewer chokes through machine learning.” Water e-Journal 2 (4): 1–13. https://doi.org/10.21139/wej.2017.035.
Department of Planning and Local Government. 2010. “Wastewater management.” In Water sensitive urban design technical manual for the greater adelaide region. Adelaide, SA: Government of South Australia.
Dirksen, J., F. H. L. R. Clemens, H. Korving, F. Cherqui, P. Le Gauffre, T. Ertl, H. Plihal, K. Müller, and C. T. M. Snaterse. 2013. “The consistency of visual sewer inspection data.” Struct. Infrastruct. Eng. 9 (3): 214–228. https://doi.org/10.1080/15732479.2010.541265.
Dirksen, J., A. Goldina, J. ten Veldhuis, and F. Clemens. 2009. “The role of uncertainty in urban drainage decisions: Uncertainty in inspection data and their impact on rehabilitation decisions.” In Strategic asset management of water supply and wastewater infrastructures, 273–286. London: International Water Association.
Dix, L., E. Hack, A. Russo, and N. Do. 2021. Tackling wastewater network overflow through monitoring and proactive intervention. London: Australian Water Association.
Do, N. C. 2017. “Genetic algorithms and particle filtering for calibrating water demands and locating partially closed valves in water distribution systems.” Ph.D. thesis, School of Civil, Environmental and Mining Engineering, Univ. of Adelaide.
Gong, J., M. F. Lambert, M. L. Stephens, B. S. Cazzolato, and C. Zhang. 2020. “Detection of emerging through-wall cracks for pipe break early warning in water distribution systems using permanent acoustic monitoring and acoustic wave analysis.” Water Resour. Manage. 34 (8): 2419–2432. https://doi.org/10.1007/s11269-020-02560-1.
Kurma, S. S., D. M. Abraham, M. R. Jahanshahi, T. Iseley, and J. Starr. 2018. “Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks.” Autom. Constr. 91 (Jan): 273–283. https://doi.org/10.1016/j.autcon.2018.03.028.
Marchi, A., A. R. Simpson, M. A. Thyer, S. Sutanto, and N. C. Do. 2012. “Optimisation of pump operations in water distribution systems taking into account the seasonality of the demand.” In Proc., 14th Water Distribution Systems Analysis Conf., 602–612. Barton, Australia: Engineers Australia.
Phillips, P. J., A. T. Chalmers, J. L. Gray, D. W. Kolpin, W. T. Foreman, and G. R. Wall. 2012. “Combined sewer overflows: An environmental source of hormones and wastewater micropollutants.” Environ. Sci. Technol. 46 (10): 5336–5343. https://doi.org/10.1021/es3001294.
Radke, R. J., S. Andra, O. Al-Kofahi, and B. Roysam. 2005. “Image change detection algorithms: A systematic survey.” IEEE Trans. Image Process. 14 (3): 294–307. https://doi.org/10.1109/TIP.2004.838698.
Reeves, J., J. Chen, X. L. Wang, R. Lund, and Q. Q. Lu. 2007. “A review and comparison of changepoint detection techniques for climate data.” J. Appl. Meteorol. Climatol. 46 (6): 900–915. https://doi.org/10.1175/JAM2493.1.
Romanova, A., K. V. Horoshenkov, S. J. Tait, and T. Ertl. 2013. “Sewer inspection and comparison of acoustic and CCTV methods.” Proc. Inst. Civ. Eng. 166 (2): 70–80. https://doi.org/10.1680/wama.11.00039.
Rybach, D., C. Gollan, R. Schluter, and H. Ney. 2009. “Audio segmentation for speech recognition using segment features.” In Proc., 2009 IEEE Int. Conf. on Acoustics, Speech and Signal Processing. New York: IEEE.
Savic, D., O. Giustolisi, L. Berardi, W. Shepherd, S. Djordjevic, and A. Saul. 2006. “Modelling sewer failure by evolutionary computing.” Proc. Inst. Civ. Eng. Water Manage. 159 (2): 111–118. https://doi.org/10.1680/wama.2006.159.2.111.
Staudacher, M., S. Telser, A. Amann, H. Hinterhuber, and M. Ritsch-Marte. 2005. “A new method for change-point detection developed for on-line analysis of the heart beat variability during sleep.” Physica A 349 (3–4): 582–596. https://doi.org/10.1016/j.physa.2004.10.026.
Stephens, M., J. Gong, C. Zhang, A. Marchi, L. Dix, and M. F. Lambert. 2020a. “Leak-before-break main failure prevention for water distribution pipes using acoustic smart water technologies: Case study in Adelaide.” J. Water Resour. Plann. Manage. 146 (10): 05020020. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001266.
Stephens, M., R. Zhang, L. Dix, N. Arbon, M. F. Lambert, A. Russo, and J. Gong. 2020b. Creating a machine learning software platform for improved operations and asset management in SA water. St Leonards, NSW, Australia: Australian Water Association.
Tchobanoglus, G., F. Burton, and H. D. Stensel. 2003. “Wastewater engineering: Treatment and reuse.” Am. Water Works Assoc. J. 95 (5): 201.
Thyer, M., T. Micevski, G. Kuczera, and P. Coombes. 2011. “A behavioural approach to stochastic end use modelling.” In Proc., Australia’s National Water Conf. and Exhibition. St Leonards, NSW, Australia: Australian Water Association.
Tscheikner-Gratl, F., et al. 2019. “Sewer asset management—State of the art and research needs.” Urban Water J. 16 (9): 662–675. https://doi.org/10.1080/1573062X.2020.1713382.
Wang, X. L. 2008. “Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or F test.” J. Appl. Meteorol. Climatol. 47 (9): 2423–2444. https://doi.org/10.1175/2008JAMC1741.1.
Yang, P., G. Dumont, and J. M. Ansermino. 2006. “Adaptive change detection in heart rate trend monitoring in anesthetized children.” IEEE Trans. Biomed. Eng. 53 (11): 2211–2219. https://doi.org/10.1109/TBME.2006.877107.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 149 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 5, 2021
Accepted: Sep 8, 2022
Published online: Nov 14, 2022
Published in print: Jan 1, 2023
Discussion open until: Apr 14, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Jinzhe Gong, Joshua S. Sim, Benny Zuse Rousso, Lloyd H. C. Chua, Investigation on the Water Depth of Choked Flow due to Bottom Blockages in Circular Open Channels, Journal of Hydraulic Engineering, 10.1061/JHEND8.HYENG-13905, 150, 5, (2024).