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Designing an Optimized Water Quality Monitoring Network with Reserved Monitoring Locations

1
School of Information Science and Technology, Nantong University, Nantong 226019, China
2
Nantong Research Institute for Advanced Communication Technologies, Nantong 226019, China
3
Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
4
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK
5
Research Institute of New-type Urbanization, Huai’an 223005, China
6
Department of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(4), 713; https://doi.org/10.3390/w11040713
Received: 10 March 2019 / Revised: 26 March 2019 / Accepted: 3 April 2019 / Published: 6 April 2019
(This article belongs to the Section Water Quality and Ecosystems)
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Abstract

The optimized design of water quality monitoring networks can not only minimize the pollution detection time and maximize the detection probability for river systems but also reduce redundant monitoring locations. In addition, it can save investments and costs for building and operating monitoring systems as well as satisfy management requirements. This paper aims to use the beneficial features of multi-objective discrete particle swarm optimization (MODPSO) to optimize the design of water quality monitoring networks. Four optimization objectives: minimum pollution detection time, maximum pollution detection probability, maximum centrality of monitoring locations and reservation of particular monitoring locations, are proposed. To guide the convergence process and keep reserved monitoring locations in the Pareto frontier, we use a binary matrix to denote reserved monitoring locations and develop a new particle initialization procedure as well as discrete functions for updating particle’s velocity and position. The storm water management model (SWMM) is used to model a hypothetical river network which was studied in the literature for comparative analysis of our work. We define three pollution detection thresholds and simulate pollution events respectively to obtain all the pollution detection time for all the potential monitoring locations when a pollution event occurs randomly at any potential monitoring locations. Compared to the results of an enumeration search method, we confirm that our algorithm could obtain the Pareto frontier of optimized monitoring network design, and the reserved monitoring locations are included to satisfy the management requirements. This paper makes fundamental advancements of MODPSO and enables it to optimize the design of water quality monitoring networks with reserved monitoring locations. View Full-Text
Keywords: multi-objective discrete particle swarm optimization; water quality monitoring network; optimized monitoring network design; reserved monitoring locations; storm water management model multi-objective discrete particle swarm optimization; water quality monitoring network; optimized monitoring network design; reserved monitoring locations; storm water management model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhu, X.; Yue, Y.; Wong, P.W.; Zhang, Y.; Ding, H. Designing an Optimized Water Quality Monitoring Network with Reserved Monitoring Locations. Water 2019, 11, 713.

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