Burst detection in water distribution systems: The issue of dataset collection

Developing data-driven models for bursts detection is currently a demanding challenge for efficient and sustainable management of water supply systems. This study focuses on the issue of collecting water consumption data affected by bursts. The aim is to present a proper methodology for a reliable generation of data, which are fundamental to set alarms and train anomaly detection models. The proposed procedure consists of stochastic modeling of water request and hydraulic pipes bursts simulation to yield suitable synthetic time series of flow rates, for instance, inlet flows of district metered areas and small water supply systems. The water request is obtained through the superimposition of different components, such as the daily, the weekly, and the yearly trends jointly with a random normal distributed component based on the consumption mean and variance, and the number of users aggregation. The resulting request is implemented into the hydraulic model of the distribution system, also embedding background leaks and bursts using a pressure-driven approach with both concentrated and distributed demand schemes. This work seeks to close the gap in the field of synthetic generation of drinking water consumption data, by establishing a proper dedicated methodology that aims to support future water smart grids.