Journal
QUIMICA NOVA
Volume 36, Issue 6, Pages 783-789Publisher
SOC BRASILEIRA QUIMICA
DOI: 10.1590/S0100-40422013000600007
Keywords
particle swarm optimization; artificial neural networks; pollutants' concentration time series
Categories
Funding
- FACEPE
- CNPq
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available