Leak detection in water distribution networks via pressure analysis using a machine learning ensemble - Fuentes, Vivencio C. Jr
Water distribution networks (WDNs) are vital infrastructure which serve as a means of public utilities to deliver potable water to consumers. Recently, a water shortage has hit the Philippine capital, with numerous households not getting enough supply for daily consumption. As the global population grows, there is a need to make water distribution efficient, due to the increasing demand. Naturally, pipelines degrade over time, causing leakages and pipe bursts. Damaged pipelines allow water to leak through, incurring significant economic losses. Mitigating these losses are important, especially in areas with water scarcity, to allow consumers to have adequate water supply. Thus, leak detection and leak localization are important in reducing system loss of the network and increasing its efficiency. Monitoring WDNs effectively for leakage is often a challenging task due to the size of the area it covers, and due to the need to detect leaks as early as possible. Traditionally, this is done via pipeline inspection or physical modeling. However, such techniques are either time-consuming, resource intensive, or both. An alternative is machine learning (ML), which maps the relationship between pipeline data to detect and locate leakages. This allows for a faster, yet reasonably accurate model for detection and localization. Data simulations are used to generate datasets for machine learning, which makes the model robust in the absence of existing real-world data. Machine learning techniques could be utilized together as an ensemble, which allows these techniques to work in conjunction with each other. Wavelet decomposition will be performed on the data to allow for a smaller dataset, as well as utilizing possible hidden features for the machine learning model. Different parameters in the ML model could be optimized to achieve optimal results.