Designing an optimal water quality monitoring network

Xiaohui Zhu; Yong Yue; Yixin Zhang; Prudence W.H. Wong; Jianhong Tan

doi:10.1007/978-3-319-68121-4_45

Designing an optimal water quality monitoring network

Xiaohui Zhu
, Yong Yue^*
, Yixin Zhang
, Prudence W.H. Wong
, Jianhong Tan

^*Corresponding author for this work

Department of Computing

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

The optimal design of water quality monitoring network can improve the monitoring performance. In addition, it can reduce the redundant monitoring locations and save the investment and costs for building and operating the monitoring system. This paper modifies the original Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the design of water quality monitoring network based on three optimization objectives: minimum pollution detection time, maximum pollution detection probability and maximum centrality of monitoring locations. We develop a new initialization procedure as well as a discrete velocity and position updating function to optimize the design of water quality monitoring network. 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 simulate pollution events in SWMM to obtain all the pollution detection time for all the potential monitoring locations. Experimental results show that the modified MOPSO can obtain steady Pareto frontiers and better optimal deployment solutions than genetic algorithm (GA).

Original language	English
Title of host publication	Intelligence Science I - 2nd IFIP TC 12 International Conference, ICIS 2017, Proceedings
Editors	Zhongzhi Shi, Ben Goertzel, Jiali Feng
Publisher	Springer New York LLC
Pages	417-425
Number of pages	9
ISBN (Print)	9783319681207
DOIs	https://doi.org/10.1007/978-3-319-68121-4_45
Publication status	Published - 2017
Event	2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017 - Shanghai, China Duration: 25 Oct 2017 → 28 Oct 2017

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	510
ISSN (Print)	1868-4238

Conference

Conference	2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017
Country/Territory	China
City	Shanghai
Period	25/10/17 → 28/10/17

Keywords

Closeness centrality
Multi-objective optimization
Optimal water quality monitoring network
SWMM

Access to Document

10.1007/978-3-319-68121-4_45

Cite this

Zhu, X., Yue, Y., Zhang, Y., Wong, P. W. H., & Tan, J. (2017). Designing an optimal water quality monitoring network. In Z. Shi, B. Goertzel, & J. Feng (Eds.), Intelligence Science I - 2nd IFIP TC 12 International Conference, ICIS 2017, Proceedings (pp. 417-425). (IFIP Advances in Information and Communication Technology; Vol. 510). Springer New York LLC. https://doi.org/10.1007/978-3-319-68121-4_45

@inproceedings{fc0cc4f098ba40e983e3f32505d143b7,

title = "Designing an optimal water quality monitoring network",

abstract = "The optimal design of water quality monitoring network can improve the monitoring performance. In addition, it can reduce the redundant monitoring locations and save the investment and costs for building and operating the monitoring system. This paper modifies the original Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the design of water quality monitoring network based on three optimization objectives: minimum pollution detection time, maximum pollution detection probability and maximum centrality of monitoring locations. We develop a new initialization procedure as well as a discrete velocity and position updating function to optimize the design of water quality monitoring network. 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 simulate pollution events in SWMM to obtain all the pollution detection time for all the potential monitoring locations. Experimental results show that the modified MOPSO can obtain steady Pareto frontiers and better optimal deployment solutions than genetic algorithm (GA).",

keywords = "Closeness centrality, Multi-objective optimization, Optimal water quality monitoring network, SWMM",

author = "Xiaohui Zhu and Yong Yue and Yixin Zhang and Wong, \{Prudence W.H.\} and Jianhong Tan",

note = "Funding Information: Acknowledgements. This work was partly supported by the Natural Science Foundation of Jiangsu Province (BK20151245), the Natural Science Foundation of Huai{\textquoteright}an City (HAG2015007), the Natural Science Foundation of Nantong City (MS12016048). Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2017.; 2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017 ; Conference date: 25-10-2017 Through 28-10-2017",

year = "2017",

doi = "10.1007/978-3-319-68121-4\_45",

language = "English",

isbn = "9783319681207",

series = "IFIP Advances in Information and Communication Technology",

publisher = "Springer New York LLC",

pages = "417--425",

editor = "Zhongzhi Shi and Ben Goertzel and Jiali Feng",

booktitle = "Intelligence Science I - 2nd IFIP TC 12 International Conference, ICIS 2017, Proceedings",

}

Zhu, X , Yue, Y, Zhang, Y, Wong, PWH & Tan, J 2017, Designing an optimal water quality monitoring network. in Z Shi, B Goertzel & J Feng (eds), Intelligence Science I - 2nd IFIP TC 12 International Conference, ICIS 2017, Proceedings. IFIP Advances in Information and Communication Technology, vol. 510, Springer New York LLC, pp. 417-425, 2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017, Shanghai, China, 25/10/17. https://doi.org/10.1007/978-3-319-68121-4_45

Designing an optimal water quality monitoring network. / Zhu, Xiaohui ; Yue, Yong; Zhang, Yixin et al.
Intelligence Science I - 2nd IFIP TC 12 International Conference, ICIS 2017, Proceedings. ed. / Zhongzhi Shi; Ben Goertzel; Jiali Feng. Springer New York LLC, 2017. p. 417-425 (IFIP Advances in Information and Communication Technology; Vol. 510).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Zhu, Xiaohui

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AU - Zhang, Yixin

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AU - Tan, Jianhong

N1 - Funding Information: Acknowledgements. This work was partly supported by the Natural Science Foundation of Jiangsu Province (BK20151245), the Natural Science Foundation of Huai’an City (HAG2015007), the Natural Science Foundation of Nantong City (MS12016048). Publisher Copyright: © IFIP International Federation for Information Processing 2017.

PY - 2017

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N2 - The optimal design of water quality monitoring network can improve the monitoring performance. In addition, it can reduce the redundant monitoring locations and save the investment and costs for building and operating the monitoring system. This paper modifies the original Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the design of water quality monitoring network based on three optimization objectives: minimum pollution detection time, maximum pollution detection probability and maximum centrality of monitoring locations. We develop a new initialization procedure as well as a discrete velocity and position updating function to optimize the design of water quality monitoring network. 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 simulate pollution events in SWMM to obtain all the pollution detection time for all the potential monitoring locations. Experimental results show that the modified MOPSO can obtain steady Pareto frontiers and better optimal deployment solutions than genetic algorithm (GA).

AB - The optimal design of water quality monitoring network can improve the monitoring performance. In addition, it can reduce the redundant monitoring locations and save the investment and costs for building and operating the monitoring system. This paper modifies the original Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the design of water quality monitoring network based on three optimization objectives: minimum pollution detection time, maximum pollution detection probability and maximum centrality of monitoring locations. We develop a new initialization procedure as well as a discrete velocity and position updating function to optimize the design of water quality monitoring network. 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 simulate pollution events in SWMM to obtain all the pollution detection time for all the potential monitoring locations. Experimental results show that the modified MOPSO can obtain steady Pareto frontiers and better optimal deployment solutions than genetic algorithm (GA).

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BT - Intelligence Science I - 2nd IFIP TC 12 International Conference, ICIS 2017, Proceedings

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A2 - Goertzel, Ben

A2 - Feng, Jiali

PB - Springer New York LLC

T2 - 2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

Y2 - 25 October 2017 through 28 October 2017

ER -

Designing an optimal water quality monitoring network

Abstract

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Keywords

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