Model-based clustering and alignment of water quality curves with prior knowledge integration using hidden Markov random fields

The distribution of high-quality drinking water is a major challenge for water distribution network operators. In this context, we propose an algorithm for contamination warning systems that partitions the network into areas of homogeneous water quality, thereby facilitating the identification of contaminated areas. To this end, we introduce a dedicated model-based approach that simultaneously clusters and aligns the conductivity signals measured at the network probes. We use the shifts inferred by the model to summarize the water propagation in a cluster. To produce physically meaningful clusters, we integrate expert knowledge about the physics of the signals into the curve clustering problem using a Hidden Markov Random Field (HMRF). We present a methodology for model selection and apply our approach to real conductivity curves from a large water distribution network. • Incorporate prior knowledge in the simultaneous clustering and alignment of curves. • Apply to water conductivity curves to find areas of homogeneous water quality. • Summarize the water propagation in a cluster using the inferred shifts.