Pressure-driven burst leakage detection using sparrow search algorithm optimized probabilistic neural networks

ABSTRACT The companies responsible for water supply networks are making significant efforts to improve leakage detection efficiency. This study proposes a novel leak localization method. First, the pressure-driven background leakage model (PDBLM) is established using a nonlinear genetic algorithm that considers the relationship between background leakage and pressure. Second, a fuzzy c-means clustering algorithm optimized by a genetic simulated annealing algorithm (FCMGSAA) and sparrow search algorithm optimized probabilistic neural networks (SSAPNNs) are chosen for the leak location model. To reduce the output classes, a large water supply network is divided into several areas using an FCMGSAA. The areas are used as learning labels, reducing the output classes of the SSA. Then, the leak location model is used to detect the leak area based on a real case in Qingdao, China. Compared with generalized regression neural networks (GRNNs), support vector machine (SVM), convolutional neural networks (CNNs) and back-propagation neural networks (BPNNs), the accuracy of the proposed method is improved by 70, 43.33, 10 and 6.67%, respectively. The proposed method can improve leakage detection efficiency, reduce water loss and contribute to the effective management of water resources.