An Improved Ensemble Deep Learning Model for Water Leakage Detection

The leakage location for water supply system is a key point for the efficient operation and maintenance of urban water supply system. However, the limited source of leakage dataset, high cost of leakage simulation experiments and the limited prediction accuracy of traditional models increase the difficulty of the leakage location for urban water supply system. This paper proposes a new method based on ensemble learning method to address these problems. EPANET software is employed in this paper for the generation of experimental dataset. The leakage is located and predicted based on the hydraulic model of the water supply system through the design of the improved multi-model weighted voting mechanism for ensemble deep learning method. The experiments show that the proposed method has higher precision and lower discretization compared to traditional models.