# Burst Detection in Water Distribution Systems:
## 1 Introduction  
The water distribution infrastructures, along with the energy grids, have undertaken an important renewal process in the last few years that aims to transform the current distribution networks in smart grids [1; 2]. The reasons for this changing lie in the necessity of water distribution systems (WDS, hereafter) to tackle water scarcity because of climate change and to deal with water requests increasing due to population growth in urban areas [3]. Therefore, water-smart networks comprise smart sensors (e.g., flow, pressure and quality meters, and noise loggers) and communication and data storage devices together with data management and analysis routines [4]. Despite the potential benefits of smart systems [2], above all the enhancement of WDS management, proper handling of the data stream through the different components represents a real challenge nowadays [5]. Smart WDS enables improving the control of water leakages which are a demanding task well known in the literature. Water leaks comprise the background losses and pipes bursts [6; 7]. The formerare characterised by a high number of small losses spread along the distribution network, which are mainly caused by the time deterioration of the pipes, valves, fittings, fire hydrants, pumps, and service connections, while the latter are defined as abrupt cracks with a considerable amount of water outflow and a limited duration over time. Thus, quick identification and localization of water pipe bursts is crucial for decreasing the amount of water losses and limiting the service interruptions [8]. At this aim, the data stream provided by the smart water grids plays a crucial role in providing the necessary inputs to leaks detection systems. Consequently, the efficiency of leaks detection depends on two main factors: on one hand, the quality and frequency of the data collected by the smart system, and, on the other, the accuracy and the reliability of the bursts detection algorithms. In recent years, the growth of available data has given a significant boost to the development of suitable leaks detection algorithms. Several data-driven methods, which aim to identify abnormal water consumption, have been proposed based on inlet flow rates and pressure measurements. In fact, water consumption follows a typical pattern in an ideal scenario. However, sudden events, such as bursts and hydrants use, can generate sharp changes. Therefore, these unusual spikes are identified as outliers in the water consumption time series and can be detected through an anomaly detection approach [9]. According to [PERSON] and [PERSON] [10], the data-driven methods adopted in bursts detection can be divided into classification methods, prediction methods, and statistical methods. The first group of models is based on identifying the data with bursts among the normal data (normal data means data not affected by unusual behaviors). At this scope, for instance, static and time delay artificial neural networks (ANN, hereafter) are used by [PERSON] and [PERSON] [11], while [PERSON] et al. [12] introduced an alarm system that relies on self-organizing map ANN. The second methods rely on a prediction phase of normal data followed by a classification: when a burst occurs, the prediction greatly differs from the measured data. A remarkable number of models has been developed belonging in this group, such as those ones based on Kalman filter [13; 14], support vector regression [15; 16], and ANN [11; 17; 18]. Instead, the third group includes the models based on statistic theories. In this group, the method mainly used is the statistical process control (SPC, hereafter) that allows for identifying variations in the data stream by means of control charts and graphs given control thresholds [19; 20]. Despite the different classes of methods, suitable data are fundamental for the proper development of such detection models [21]. Specifically, normal data and data affected by bursts (called also abnormal data) are necessary for several purposes.