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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).
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Xiaohui Zhu, Yong Yue, Yixin Zhang, Prudence Wong, Jianhong Tan. Designing an Optimal Water Quality Monitoring Network. 2nd International Conference on Intelligence Science (ICIS), Oct 2017, Shanghai, China. pp.417-425, ⟨10.1007/978-3-319-68121-4_45⟩. ⟨hal-01820934⟩



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