Climate change is expected to cause more frequent and intense weather events such as droughts and floods, which can place additional stress on water distribution networks (WDN). Leakage in water distribution networks is a significant challenge that exacerbate the effects of climate change by increasing the amount of water that needs to be extracted and treated, as well as increasing energy consumption and greenhouse gas emissions associated with pumping and treating water. Therefore, accurate leakage localization can help reduce the amount of water lost from distribution networks, thereby reducing the need for additional water extraction and treatment. This can lead to energy savings and reduced greenhouse gas emissions, as well as ensuring that water resources are used efficiently and effectively. Additionally, by reducing the amount of water lost to leakage, WDN can be made more resilient to the impacts of climate change, such as droughts and water scarcity.
State-of-the-art methods tackle the challenge of leakage localization in a WDN comprise acoustic methods [1], pressure transient methods [2], flow measurement methods [3], and machine learning (ML) based methods [4-5]. However, these methods have significant limitations that hinder their application in the daily routine of water utilities. For instance, acoustic methods are cost intensive as they require additional sensors and equipment. Furthermore, the accuracy of such methods is greatly affected by the material of the pipes and presence of noise. Although the sensors that are necessary for the pressure transient methods and flow measurement methods might available due to the daily operation of WDN, these methods are often not sensitive to detect small leaks. Data-driven methods using ML has gain more importance in the recent year. However, the data availability, data quality and the explainability of ML models are the major limitations.
Therefore, we would like to investigate the effectiveness of a hybrid AI approach combining hydraulic model and ML to tackle the leakage localization within WDN using real world data. The following aspects need to be considered:
• Literature review of leakage localization for WDN.
• Development and implementation of a hybrid framework combining hydraulic model and ML methods for leakage localization with existing sensor data.
• Comprehensive evaluation of the performance of the implemented framework w.r.t. accuracy, robustness, and explainability.
[1] Khulief, Yehia et. al., (2012). Acoustic Detection of Leaks in Water Pipelines Using Measurements inside Pipe. ASCE Journal of Pipeline System Engineering and Practice. 2021, 3, 47. Doi:10.1061/(ASCE)PS.1949-1204.0000089.
[2] Levinas, D. et. al., Water Leak Localization Using High-Resolution Pressure Sensors. Water 2021, 13, 591. https://doi.org/10.3390/w13050591
[3] L. Lindström, et. al. Leakage Localization in Water Distribution Networks: A Model-Based Approach, 2022 European Control Conference (ECC), London, United Kingdom, 2022, pp. 1515-1520, doi: 10.23919/ECC55457.2022.9838006.
[4] Huang, Pingjie, et al. “Real-time burst detection in district metering areas in water distribution system based on patterns of water demand with supervised learning.” Water 10.12 (2018): 1765. doi.org/10.3390/w10121765
[5] Soldevila, Adrià, et al. “Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation.” Water 11.7 (2019): 1500. doi.org/10.3390/w11071500
