title: Wavelet transform-based artificial neural networks (WT-ANN) in PM10 pollution level estimation, based on circular variables.
abstract: BACKGROUND
  In this paper, a novel method in the estimation and prediction of PM(10) is introduced using wavelet transform-based artificial neural networks (WT-ANN).
  
  
  CONCLUSIONS
  First, the application of wavelet transform, selected for its temporal shift properties and multiresolution analysis characteristics enabling it to reduce disturbing perturbations in input training set data, is presented. Afterward, the circular statistical indices which are used in this method are formally introduced in order to investigate the relation between PM(10) levels and circular meteorological variables. Then, the results of the simulation of PM(10) based on WT-ANN by use of MATLAB software are discussed. The results of the above-mentioned simulation show an enhanced accuracy and speed in PM(10) estimation/prediction and a high degree of robustness compared with traditional ANN models.
authors: Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran. maryam_shekarriz@yahoo.ca
authors: Maryam
authors: M
authors: Shekarrizfard
authors: A
authors: A
authors: Karimi-Jashni
authors: K
authors: K
authors: Hadad
mesh_terms: D000392:Air Movements; D000393:Air Pollutants / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D007492:Iran; D008685:Meteorological Concepts; D016571:Neural Networks, Computer; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D013997:Time Factors
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter