Gradual Leak Detection in Water Distribution Networks Based on Multistep Forecasting Strategy
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 8
Abstract
With the availability of real-time monitoring data, leakage detection for water distribution networks (WDNs) based on data-driven methods has received increasing attention in recent years. Accurate forecasts based on historical data could provide valuable information about the condition of the WDN, and abnormal events could be detected if the observed behavior is substantially different from the typical behavior. Therefore, an accurate forecast model is essential for prediction-based leakage detection methods. While most data-driven methods focus on burst detection, it is also important to develop an early warning system for gradual leakage events because they will cause more water loss due to a longer time to awareness. Therefore, a real-time early leakage detection technique based on a multistep forecasting strategy is proposed in this study. A multistep flow forecasting model is introduced to capture the diurnal, weekly, and seasonal patterns in the historical data. The generated multistep forecasting is further compared with the observed measurements, and residuals are calculated based on cosine distance. Based on the analysis of the residual vector, the gradual leakage event could be detected in a timely manner. The proposed method is applied to the L-town datasets containing one year of real-life flow monitoring data. The results prove the superiority of the proposed multistep prediction model-based method over the traditional one-step prediction model for gradual leakage detection. In addition, the results show that the proposed methodology can detect small gradual leakage events within just a few days while generating no false alarms. The method was further applied to a real-life network and showed consistent results.
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Data Availability Statement
The hydraulic model used in this study is available at https://battledim.ucy.ac.cy/. The following data and the model used in this study can be made available by the corresponding author on request: data of synthetic experiments, and codes for the proposed method in Python language.
Acknowledgments
The first author is funded by the China Scholarship Council (No. 202006370080), and the work is supported by a Royal Academy of Engineering Industrial Fellowship to resource Raziyeh Farmani’s involvement (IF\192057).
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 149 • Issue 8 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 29, 2022
Accepted: Mar 30, 2023
Published online: May 30, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 30, 2023
ASCE Technical Topics:
- Climates
- Detection methods
- Distance measurement
- Engineering fundamentals
- Environmental engineering
- Flow (fluid dynamics)
- Flow patterns
- Fluid dynamics
- Fluid mechanics
- Forecasting
- Hydrologic engineering
- Mathematics
- Measurement (by type)
- Methodology (by type)
- Model accuracy
- Models (by type)
- Seasonal variations
- Statistics
- Water and water resources
- Water leakage and water loss
- Water management
- Water supply
- Water supply systems
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