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Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning



Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning



Ecological Modelling 185(2-4): 513-529



Ozone and PM10 constitute the major concern for air quality of Milan. This paper addresses the problem of the prediction of such two pollutants, using to this end several statistical approaches. In particular, feed-forward neural networks (FFNNs), currently recognized as state-of-the-art approach for statistical prediction of air quality, are compared with two alternative approaches derived from machine learning: pruned neural networks (PNNs) and lazy learning (LL).

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Accession: 011737794

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DOI: 10.1016/j.ecolmodel.2005.01.008


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