Predicting Water Pipe Failures with a Recurrent Neural Hawkes Process Model

Water distribution networks have shown an increased rate of failure due to material deterioration. In this paper, we apply a Recurrent Neural Hawkes Process model to learn the failure intensity function of water pipes. The failure intensity function is learned based on two components: the base failure rate that is determined by the unique pipe profile attributes, and the effect of past failures. Compared to the existing solutions, our model is able to predict the time to next failure on an individual water pipe level. The learned failure intensity function is used to identify value points in the deterioration process of water pipes that represent their economical end-of-life. We use data from a Dutch water distribution network that consists of 49,600 km of pipelines to test the performance of the proposed model. We have made this dataset available online.