Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Prevision of industrial SO2 pollutant concentration applying ANNs
Year:2009
Research Areas
  • Processing and signal analysis
Information
Abstract
Air pollution is one of the most important environmental problems. Sulphur Dioxide (SO2) and Suspended Particles are considered the most important atmospheric pollutants. The prevision of industrial SO2 air pollutant concentrations would allow us to take preventive measures such as reducing the pollutant emission to the atmosphere. In This work we apply Feed Forward Artificial Neural Network to predict the air pollution concentrations in Salamanca, Mexico. The work focuses on the daily maximum concentration of SO2. A database used to train the neural network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and concentrations of SO2 along a year. Results of the experiments with the proposed system show the importance of the meteorological variable set on the prediction of SO2 concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
International
Si
Congress
Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on
960
Place
Cardiff, UK
Reviewers
Si
ISBN/ISSN
1935-4576
10.1109/INDIN.2009.5195856
Start Date
23/06/2009
End Date
26/06/2009
From page
510
To page
515
Proc. of Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on
Participants
  • Autor: Antonio Vega-Corona (UG)
  • Autor: María Guadalupe Cortina Januchs (UPM)
  • Autor: Diego Andina De la Fuente (UPM)
  • Autor: Jose Miguel Barron Adame (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Automatización en Señal y Comunicaciones (GASC)
  • Departamento: Señales, Sistemas y Radiocomunicaciones
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