Neural network model for maximum ozone concentration prediction (original) (raw)

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.

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Authors and Affiliations

1. CECTA, Universidad de Santiago de Chile, Chile
   Gonzalo Acuña
2. Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Chile
   Héctor Jorquera & Ricardo Pérez

Authors

1. Gonzalo Acuña
2. Héctor Jorquera
3. Ricardo Pérez

Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Acuña, G., Jorquera, H., Pérez, R. (1996). Neural network model for maximum ozone concentration prediction. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5\_47

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• DOI: https://doi.org/10.1007/3-540-61510-5\_47
• Published: 09 June 2005
• Publisher Name: Springer, Berlin, Heidelberg
• Print ISBN: 978-3-540-61510-1
• Online ISBN: 978-3-540-68684-2
• eBook Packages: Springer Book Archive

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