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Neural network model for maximum ozone concentration prediction

  • Gonzalo Acuña
  • Héctor Jorquera
  • Ricardo Pérez
Oral Presentations: Applications Practical Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1112)

Abstract

A neural network dynamic model was used for predicting maximum ozone (O3) concentration at Santiago de Chile. Learning and test data were collected during summer and springtime periods of 1990, 1992 and 1993. A neural network having O3 t, Tt+1 (maximum air temperature) and Tt as inputs for predicting O3 t+1 was chosen because of its low test error. This neural network model greatly reduces the error coming from a pure persistence model when applied to the generalization set of data (1994). Long-term predictions results confirm the good concordance obtained between the observed and forecasted values thus showing the adequacy of neural networks to model the dynamics of this complex environmental phenomena.

Keywords

Neural networks ozone forecasting dynamic modeling predictive model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Gonzalo Acuña
    • 1
  • Héctor Jorquera
    • 2
  • Ricardo Pérez
    • 2
  1. 1.CECTAUniversidad de Santiago de ChileChile
  2. 2.Departamento de Ingeniería Química y BioprocesosPontificia Universidad Católica de ChileChile

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