Optimum Water Quality Monitoring Network Design for Bidirectional River Systems Using Modified MOPSO

Abstract. Designing an optimum water quality monitoring network will not only minimize the pollution detection time and maximize the detection probability in river systems, but also reduce the redundant monitoring nodes and save the investment and costs for building and running the network. We propose a novel method for the optimum water quality monitoring network design and identification of the influence of bidirectional water flows which has not be studied in the literature. In order to handle discrete issues of designing an optimum water quality monitoring network for bidirectional rivers, we have modified the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and developed new fitness functions. The Storm Water Management Model (SWMM) is used to simulate pollution events of a hypothetical river network which was studied in the literature for comparative analysis of our work. Simulation results show that the modified MOPSO can obtain a better Pareto frontier whilst bidirectional water flows have a significant effect on the optimization monitoring network design. We achieve a different optimum deployment from unidirectional water flow for the same river system. We also find that the probability of bidirectional water flows has no effect on the optimum monitoring network design but the pollution detection threshold of the monitoring devices can affect the network design when the threshold is high.