Optimizing stations location for urban noise continuous intelligent monitoring

Abstract

In most of urban noise monitoring systems, the optimization of number and locations of autonomous monitoring stations is a Non-deterministic Polynomial Complete (NPC) problem. It is also important for the implementation of intelligent measurement networks. This paper investigates an optimization method to achieve minimum stations for urban noise intelligent monitoring. First a mathematical model for monitoring stations selection has been developed. Next, a novel hybrid Immune PSO K-means (IPKM) clustering algorithm is proposed to solve the mathematical model. The IPKM algorithm can overcome the shortcomings (e.g. slow convergence speed) of the Particle Swarm Optimization (PSO) algorithm, and help K-means clustering algorithm escaping from local optima. Finally, the methodology has been applied to QingDao urban noise intelligent monitoring networks. For comparison, the K-means algorithm and IPKM algorithm are applied to the noise grid survey datasets of 1998–2014years. The final optimized results illustrate the proposed method could perform relevant monitoring tasks with fewer monitoring stations. In addition, the importance of the proposed method is that it would be applicable for noise monitoring and noise control management problems.