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Your search keyword '"Piazza, V."' showing total 15 results
Start Over Author "Piazza, V." Publisher elsevier bv
15 results on '"Piazza, V."'

5. Ingestion and contact with polyethylene microplastics does not cause acute toxicity on marine zooplankton

Author

Beiras, R., primary, Bellas, J., additional, Cachot, J., additional, Cormier, B., additional, Cousin, X., additional, Engwall, M., additional, Gambardella, C., additional, Garaventa, F., additional, Keiter, S., additional, Le Bihanic, F., additional, López-Ibáñez, S., additional, Piazza, V., additional, Rial, D., additional, Tato, T., additional, and Vidal-Liñán, L., additional

Published
2018
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15. Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy

Author

Salvatore Vitabile, L. Pignato, Filippo Sorbello, V. Piazza, U. Brunelli, BRUNELLI, U, PIAZZA, V, PIGNATO, L, SORBELLO, F, and VITABILE, S

Subjects
Atmospheric Science, Recurrent neural network, Artificial neural network, Correlation coefficient, Meteorology, Mean squared error, Stochastic modelling, Forecast skill, Statistical dispersion, Air quality index, General Environmental Science, Mathematics
Abstract

Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between 1 January 2003 to 31 December 2004 in eight monitoring stations in urban area of Palermo (Italy). Experimental trials show that the developed and tuned model is appropriate, giving small values of root mean square error (RMSE) , mean absolute error (MAE) and mean square error (MSE). In addition, the related correlation coefficient ranges from 0.72 to 0.97 for each forecasted pollutant, underlying a small difference between the forecasted and the measured values. The above results make the proposed forecaster a powerful tool for pollution management systems.

Published
2007

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