Data-driven pollution source location algorithm in water quality monitoring sensor networks

Abstract

Water pollution prevention has been a widely concerned issue for the safety of human lives. To this end, water quality monitoring sensors are introduced in the water distribution systems. Due to the limited budget, it is impossible to deploy sensors everywhere but a small number of sensors are deployed. From the sparse sensor data, it is important, but also challenging, to find out the pollution source location. Traditional methods may suffer from local optimum trapping or low localisation accuracy. To address such problems, we propose a cooperative intelligent optimisation algorithm-based pollution source location algorithm, which is a data-driven approach in simulation-optimisation paradigm. Through open-source EPANET simulator-based experiments, we find out our proposed data-driven algorithm can effectively and efficiently localise the pollution location, as well as the pollution injection starting time, duration and mass.