Short-term Ozone Forecasting by Artificial Neural Networks
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CONTRIBUTORS:
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JOURNAL:
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Advances in Engineering Software,
23(3),
143 -
150.
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YEAR:
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1995
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PUB TYPE:
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Journal Article
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SUBJECT(S):
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Neural Networks,
Forecasting,
Ozone,
Mexico City
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DISCIPLINE:
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Computer Science
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HTTP:
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LANGUAGE:
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Estonian
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PUB ID:
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103-427-292
(Last edited on
2006/06/03 15:00:44 GMT-6)
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SPONSOR(S):
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ABSTRACT:
In this work we report preliminary results of a study aiming to develop an intelligent tool for performing ozone forecasting in the polluted atmosphere of Mexico City. This tool is based in the paradigm of neural networks. Two neural models are used in this work, namely, the Bidirectional Associative Memory (BAM) and the Holographic Associative Memory (HAM). We analyse and preprocess daily patterns of meteorological variables and concentrations of pollutants to train both neural networks and then we use them to predict ozone at one point in the city. Preliminary results are reported and some conclusions are drawn.
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