Pipeline Leakage Identification Based on Acoustic Sensors and EPSO-1D-CNN-Bi-LSTM Model

Water supply pipe systems are typically buried in the ground, leakage has always been a significant problem for urban water supply systems. Although leakage detection can be performed using in-pipe inspection devices with hydrophone modules, the accuracy is low and depends on staff experience, and long-term work can harm health. Therefore, leakage detection and classification of various leakage levels are crucial for pipelines. This study presents a one-dimensional convolutional neural network and bidirectional long short-term memory network fusion model (1D-CNN-Bi-LSTM) for leakage detection, with enhanced particle swarm optimization (EPSO) algorithm optimized hyperparameters and multi-feature fusion for data enhancement. Ablation experiments show the key roles of EPSO and Bi-LSTM modules, and full-scale experiments confirm the method's effectiveness. Compared to other models, this one reaches 98.33% in both leakage detection and severity classification accuracy, with strong anti-noise ability and stable recognition. In conclusion, the proposed method reduces reliance on in pipe devices, offering a more accurate and effective solution for pipeline leakage detection and severity assessment.