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Benchmarking dataset for leak detection and localization in water distribution systems.
Mohsen Aghashahi1, Lina Sela2, M Katherine Banks3
1Texas A&M Institute of Data Science, Texas A&M University, 155 Ireland Street, College Station, TX 77843, USA.
Data in Brief
|May 2, 2023
Summary
This study introduces a new dataset for detecting and locating leaks in water systems using sensor data. This resource aids in developing better leak detection models and validating existing ones.
Area of Science:
- Engineering
- Environmental Science
- Data Science
Background:
- Water distribution systems are critical infrastructure.
- Leakage in these systems leads to significant water loss and economic impact.
- Accurate leak detection and localization are essential for efficient water management.
Purpose of the Study:
- To present a comprehensive dataset for leak detection and localization in water distribution systems.
- To provide a benchmark for developing and validating new algorithms.
- To facilitate research into faulty sensor detection.
Main Methods:
- Generated 280 sensory measurements from a lab-scale water distribution system.
- Utilized accelerometers, hydrophones, and dynamic pressure sensors.
- Simulated four leak types across looped and branched network topologies under varying background conditions.
Main Results:
- The dataset includes detailed measurements under diverse conditions.
- Each measurement is 30 seconds long with specific sampling frequencies.
- This is the first publicly available dataset of its kind.
Conclusions:
- The dataset is a valuable resource for the research community.
- It will accelerate advancements in leak detection and localization technologies.
- Enables further research in sensor reliability and data augmentation.
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