Evolution and forecasting of PM10 concentration at the Port of Gijon (Spain).

Scientific Reports
Fernando Sánchez LasherasFrancisco Javier de Cos Juez

Abstract

The name PM10 refers to small particles with a diameter of less than 10 microns. The present research analyses different models capable of predicting PM10 concentration using the previous values of PM10, SO2, NO, NO2, CO and O3 as input variables. The information for model training uses data from January 2010 to December 2017. The models trained were autoregressive integrated moving average (ARIMA), vector autoregressive moving average (VARMA), multilayer perceptron neural networks (MLP), support vector machines as regressor (SVMR) and multivariate adaptive regression splines. Predictions were performed from 1 to 6 months in advance. The performance of the different models was measured in terms of root mean squared errors (RMSE). For forecasting 1 month ahead, the best results were obtained with the help of a SVMR model of six variables that gave a RMSE of 4.2649, but MLP results were very close, with a RMSE value of 4.3402. In the case of forecasts 6 months in advance, the best results correspond to an MLP model of six variables with a RMSE of 6.0873 followed by a SVMR also with six variables that gave an RMSE result of 6.1010. For forecasts both 1 and 6 months ahead, ARIMA outperformed VARMA models.

References

Sep 10, 2004·The New England Journal of Medicine·W James GaudermanJohn Peters
Jan 1, 1997·IEEE Transactions on Neural Networks·C M RoadknightD Palmer-Brown
Aug 28, 2009·Environmental Science & Technology·Harshit AgrawalDavid R Cocker
Jan 9, 2010·Environmental Monitoring and Assessment·Cengiz DenizGökhan Civkaroglu
Jul 14, 2010·Environmental Science and Pollution Research International·Marco PandolfiTeresa Moreno
Jan 22, 2011·Journal of Occupational Medicine and Toxicology·Céline GasnierGilles-Eric Séralini
Mar 26, 2015·Sensors·Fernando Sánchez LasherasVictor Manuel González Suárez

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R
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