The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems

Leakage is one of the abnormal conditions in water distribution systems (WDSs). Real-time monitoring can be used to prevent or recover quickly from leakage. However, this is not enough: for improved leakage detection, a status diagnosis of the WDS must be performed together with this real-time monitoring, and numerous studies have been conducted on this. Furthermore, the existing proposed methodology only provides optimal sensor location and fast recognition. This paper proposes a technique that can quantitatively evaluate the volume of leakage along with leakage detection using deep learning technology. The hydraulic data (e.g., pressure, velocity, and flow) from the calibrated hydraulic model were used as training data and deep learning techniques were applied to conduct a simultaneous detection of leakage volume and location. We examined various scenarios regarding leakage volume and location for the data configuration of a simulated leakage accident. Furthermore, for optimal leakage detection performance, the detection performance according to the size of the network, the meter types of meters, the number of meters, and the locations of the meters were analyzed. This study is expected to be helpful in various aspects such as recovery and restoration decision making after leakage, because it simultaneously identifies the amount and location of the leakage.

Automated summary

This research proposes a method that quantitatively evaluates the volume of leakage using deep learning technology and simultaneously detects the location of leakage through real-time monitoring. By using hydraulic data from a calibrated hydraulic model as training data and applying deep learning techniques, the study analyzes various scenarios regarding leakage volume and location to optimize leakage detection performance.