Detection of multiple leakage points in water distribution networks based on convolutional neural networks

Currently, a total of 3.6 billion people live in water-deficient areas, and the population living in water-deficient areas may reach from 4.8 to 5.7 billion by 2050. Despite that, the water distribution system (WDS) loses an average of 35% of its water resources, and the leakage rates may reach even higher values in some regions. The dual pressures of the lack of water resources and severe WDS leakage become even more problematic considering that commonly used leakage detection methods are time-consuming, labour-intensive, and can only detect single-point leakages. For multiple leakage point detection, these methods often perform poorly. To solve the problem of multiple leakage point detection, this paper presents a method for multiple leakage point detection based on a convolutional neural network (CNN). A CNN can forecast the leakages from a macro-perspective. It extracts the features of the collected historical leakage data by constructing a CNN model and predicts whether the real-time data are leakage data or not based on the learning of the features that are extracted from the historical data. The experimental results show that the detection accuracies based on 21 sensors of one, two, and three leakage points are 99.63%, 98.58% and 95.25%, respectively. After the number of sensors is reduced to eight, the leakage detection accuracies of one, two, and three leakage points are 96.43%, 94.88% and 91.56%, respectively.