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.
Highlights
River systems play a crucial role in the sustainable development of a community
Only three optimization objectives of maximum pollution detection probability, minimum pollution detection time and maximum closeness centrality of monitoring locations should be calculated in the cost function for dominance evaluation
From subgraph (d) we can find that the objectives of minimum pollution detection time and maximum pollution detection probability collide with each other
Summary
River systems play a crucial role in the sustainable development of a community. Water quality is influenced simultaneously by both anthropogenic and natural activities. Many researchers have studied the optimal design of water quality monitoring networks for river systems. Telci argued that the design of an optimal water quality monitoring network should mainly focus on two objectives of minimum pollution detection time and maximum detection reliability [12]. The optimal placement of monitoring devices was calculated using the GA under relatively simple discrete uniform distributions on spill events They applied this methodology to the Altamaha river basin to identify the optimal monitoring locations in the river system [13]. We argue that the priorities of monitoring locations should be considered in the optimal design of water quality monitoring networks. Our algorithm can include all the reserved monitoring locations into the final optimized monitoring network while still having maximum pollution detection probability, minimum pollution detection time and maximum closeness centrality
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