Pipe bursts cause a considerable loss of treated water, increase the risks of environmental contamination and are a health hazard for the end-user as they can create a passage for contaminants to enter water distribution networks (WDN). Identifying pipe burst locations will help water service providers repair the pipe bursts in a timely manner. Given the ever-increasing importance of water, a great number of methods to locate pipe bursts have been proposed. But none have proved to produce results accurate enough for water service providers to heavily rely on. Therefore, this project aims to develop a fully-linear ResNet (FL-ResNet) that accurately locates pipe bursts using pressure sensors. A WDN modelling software EPANET will be used to simulate the pressure measurements to train the FL-ResNet. The performance of the algorithm will be compared to a Burst Location Identification Framework by Fully-linear DenseNet (BLIFF) and other machine learning methods. The FL-ResNet is expected to significantly contribute to the better management of water resources.