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

Authors: Xuesong Yan; Chengyu Hu; Victor S. Sheng

Addresses: School of Computer Science, China University of Geosciences, Wuhan, Hubei, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, China ' Department of Computer Science, Texas Tech University, Lubbock, TX, USA

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.

Keywords: sensor networks; pollution source location; simulation optimisation; cooperative optimisation algorithm.

DOI: 10.1504/IJBIC.2020.107474

International Journal of Bio-Inspired Computation, 2020 Vol.15 No.3, pp.171 - 180

Received: 30 Oct 2018
Accepted: 02 Sep 2019
Published online: 29 May 2020 *

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