TIME-SERIES FORECASTING OF POLLUTANT CONCENTRATION LEVELS USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORKS

被引：13
|
作者：
de Albuquerque Filho, Francisco S. [1 ]
Madeiro, Francisco [1 ]
Fernandes, Sergio M. M. [1 ]
de Mattos Neto, Paulo S. G. [2 ]
Ferreira, Tiago A. E. [3 ]
机构：
[1] Univ Catolica Pernambuco, Ctr Ciencias & Tecnol, BR-50050900 Recife, PE, Brazil
[2] Univ Fed Pernambuco, Ctr Informat, BR-50740560 Recife, PE, Brazil
[3] Univ Fed Rural Pernambuco, Dept Estatist & Informat, BR-52171900 Recife, PE, Brazil
来源：
QUIMICA NOVA | 2013年 / 36卷 / 06期
关键词：
particle swarm optimization; artificial neural networks; pollutants' concentration time series; AIR-POLLUTION; PREDICTION; MODELS; PM10; AREA; SYSTEM; NO2;
D O I：
10.1590/S0100-40422013000600007
中图分类号：
O6 [化学];
学科分类号：
0703 ;
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.
引用
收藏
页码：783 / 789
页数：7
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