Identification of Flow Pressure-Driven Leakage Zones Using Improved EDNN-PP-LCNetV2 with Deep Learning Framework in Water Distribution System.

Dong, Bo; Shu, Shihu; Li, Dengxin

This study introduces a novel deep learning framework for detecting leakage in water distribution systems (WDSs). The key innovation lies in a two-step process: First, the WDS is partitioned using a K-means clustering algorithm based on pressure sensitivity analysis. Then, an encoder–decoder neural network (EDNN) model is employed to extract and process the pressure and flow sensitivities. The core of the framework is the PP-LCNetV2 architecture that ensures the model's lightweight, which is optimized for CPU devices. This combination ensures rapid, accurate leakage detection. Three cases are employed to evaluate the method. By applying data augmentation techniques, including the demand and measurement noises, the framework demonstrates robustness across different noise levels. Compared with other methods, the results show this method can efficiently detect over 90% of leakage across different operating conditions while maintaining a higher recognition of the magnitude of leakages. This research offers a significant improvement in computational efficiency and detection accuracy over existing approaches.

WATER leakage; LEAK detection; WATER distribution; K-means clustering; DATA augmentation

Processes, 2024, Vol 12, Issue 9, p1992

2227-9717

Academic Journal

10.3390/pr12091992