Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

Química Nova

Mattos Neto, Paulo S. G. de¹  Fernandes, Sérgio M. M.¹  Ferreira, Tiago A. E.¹  Universidade Federal Rural de Pernambuco, Recife, Brasil¹  Universidade Católica de Pernambuco, Recife, Brasil¹  Universidade Federal de Pernambuco, Recife, Brasil¹  Madeiro, Francisco¹  Albuquerque Filho, Francisco S. de¹ 

关键词: particle swarm optimization;    artificial neural networks;    pollutants' concentration time series.;   

DOI  :  10.1590/S0100-40422013000600007

学科分类：化学（综合）

来源: Sociedade Brasileira de Quimica

【 摘 要 】

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

【 授权许可】

Unknown