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Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning
| Venue: | Ecological Modelling |
| Citations: | 19 - 3 self |
BibTeX
@ARTICLE{Corani_airquality,
author = {Giorgio Corani},
title = {Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning},
journal = {Ecological Modelling},
year = {},
pages = {513--529}
}
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Abstract
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). PNNs constitute a parameter-parsimonious approach, based on the removal of redundant parameters from fully connected neural networks; LL, on the other hand, is a local linear prediction algorithm, which performs a local learning procedure each time a prediction is required. All the three approaches are tested in the prediction of ozone and PM10; predictors are trained to return at 9 a.m. the concentration estimated for the current day. No strong differences are found between the forecast accuracies of the different models; nevertheless, LL provides the best performances on indicators related to average goodness of the prediction (correlation, mean absolute error, etc.), while PNNs are superior to the other approaches in detecting of the exceedances of alarm and attention thresholds. In some cases, data-deseasonalization is found to improve the prediction accuracy of the models. Finally, some striking features of lazy learning deserve consideration: the LL predictor can be quickly designed, and, thanks to the simplicity of the local linear regressors, it both gets rid of overfitting problems and can be readily interpreted; moreover, it can be also easily kept up-to-date. Key words: feed forward neural networks, pruned neural networks, lazy learning, time series prediction, atmospheric pollution.
Keyphrases
neural network lazy learning feed-forward neural network air quality prediction air quality deserve consideration end several statistical approach local linear prediction algorithm statistical prediction time series prediction forecast accuracy local linear regressors pruned neural network key word ll predictor alternative approach striking feature strong difference local learning procedure atmospheric pollution state-of-the-art approach machine learning parameter-parsimonious approach mean absolute error attention threshold redundant parameter prediction accuracy major concern different model average goodness current day
