TY - GEN
T1 - Designing an optimal water quality monitoring network
AU - Zhu, Xiaohui
AU - Yue, Yong
AU - Zhang, Yixin
AU - Wong, Prudence W.H.
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
Y1 - 2017
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).
KW - Closeness centrality
KW - Multi-objective optimization
KW - Optimal water quality monitoring network
KW - SWMM
UR - http://www.scopus.com/inward/record.url?scp=85032654727&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68121-4_45
DO - 10.1007/978-3-319-68121-4_45
M3 - Conference Proceeding
AN - SCOPUS:85032654727
SN - 9783319681207
T3 - IFIP Advances in Information and Communication Technology
SP - 417
EP - 425
BT - Intelligence Science I - 2nd IFIP TC 12 International Conference, ICIS 2017, Proceedings
A2 - Shi, Zhongzhi
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 -