As climate change intensifies rainfall and flooding, the need for smarter, more resilient sewer system management is increasingly urgent. A research team led by Prof. Tarek ZAYED, Member of Research Institute for Artificial Intelligence of Things (RIAIoT), Research Institute for Sustainable Urban Development (RISUD) and Research Centre for Resources Engineering towards Carbon Neutrality (RCRE), Professor of Department of Building and Real Estate, has developed a multi-tiered model integrating artificial intelligence (AI) and the Internet of Things (IoT) to enable more cost-effective and intelligent sewer system management. The model supports a wide range of functions, from predicting exfiltration severity and identifying leakage-prone areas to monitoring and forecasting overflow incidents in high-risk locations.
This smart management model uses deep learning algorithms to analyse condition of sewer pipelines with a high degree of accuracy. By applying these algorithms, ageing and defective sections can be identified more effectively, helping to prioritise locations for CCTV inspections.
A core component of the system is the team’s pioneering Exfiltration Severity Index (ESI), which quantifies and models pipe-level exfiltration severity, enabling sewer line managers to identify segments that prone to leakage in advance. Prof. Zayed explained that sewer exfiltration occurs when sewage escapes from a defective sewer system into the surrounding environment, which can contaminate soil or groundwater with pollutants such as pathogens and toxic compounds, posing risks to both ecosystems and public health.
The AI model incorporates a range of parameters, including pipe characteristics, climatic conditions and environmental impacts, to predict the likelihood and severity of exfiltration. This provides valuable insights for prioritising the most urgent maintenance work. The team’s research showed that the system achieved an 85% accuracy rate in severity assessment, significantly reducing the risk of groundwater contamination. In addition, through optimised maintenance scheduling, the predictive model improved operational efficiency by 50% to 60% and reduced emergency repairs by 30% to 40%. The team’s findings have been published in the journal Tunnelling and Underground Space Technology, under the title “Proactive Exfiltration Severity Management in Sewer Networks: A Hyperparameter Optimization for Two-tiered Machine Learning Prediction”.
In addition to leakage, blockage is another major cause of sewer system failures and, in more severe cases, flooding. To address this issue, Prof. Zayed’s team has applied IoT-based technologies to simulate the performance of sewer networks and overflow events under different blockage scenarios.
In collaboration with the Drainage Services Department of the Hong Kong Special Administrative Region (HKSAR) Government, the team installed water-level sensors across drainage networks in Kowloon, collecting real-world data and applying a range of data mining techniques for case study simulations, as well as for model calibration and validation. The IoT-based monitoring system delivered impressive results: sewer segments identified as having blockage issues showed an 85% improvement in overall performance following targeted cleaning.
The related study, “Performance Assessment of Sewer Networks under Different Blockage Situations Using Internet-of-things-based Technologies”, has been published in the journal Sustainability. The research was supported by the Research Grants Council’s General Research Fund and the Environment and Conservation Fund.
Press release: https://polyu.me/4tHS0VL
Online coverage:
Mirage - https://polyu.me/41nOG64
Ta Kung Pao - https://polyu.me/3OrIxmm
Wen Wei Po - https://polyu.me/3OjGn8a; https://polyu.me/41WeB4V
Bauhinia - https://polyu.me/4vivqER
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