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Environmental Monitoring and Assessment

, Volume 79, Issue 3, pp 217–230 | Cite as

Analysis of Pollutant Levels in Central Hong Kong Applying Neural Network Method with Particle Swarm Optimization

  • W. Z. Lu
  • H. Y. Fan
  • A. Y. T. Leung
  • J. C. K. Wong
Article

Abstract

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.

environment modelling neural networks particle swarm optimization pollutant 

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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • W. Z. Lu
    • 1
  • H. Y. Fan
    • 2
    • 3
  • A. Y. T. Leung
    • 1
  • J. C. K. Wong
    • 1
  1. 1.Department of Building & ConstructionCity University of Hong Kong, Kowloon Tong, KowloonHKSARPR China
  2. 2.SER Turbomachinery Research Centre, School of Energy & Power EngineeringXi'an Jiaotong UniversityXi'anPR China
  3. 3.Visiting scholar of Building and Construction DepartmentCity University of Hong KongHong Kong

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