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- W2219903919 abstract "We show the results produced with a neural network model designed to forecast tomorrow’s maximum of the 24 h average of the PM10 concentrations in the city of Santiago, Chile. We were able to generate a daily report at 8 PM based on concentrations measured until 7 PM at five stations on the present day plus measured and forecasted values of meteorological variables. The report delivers maxima for the following day at the site of the same five stations. The dominant condition is assumed to be given by the greatest of the concentrations among the five forecasts. Since, according to the range where the concentrations fall, three classes of air quality are defined, good, bad and critical, we have focused our efforts on the correct prediction of this air quality class. We have trained the models using 2001 and 2002 data to test 2003 conditions and 2002 and 2003 data in order to forecast 2004 values. According to the results reported here, 87% of the 2003 days and 92% of the 2004 cases were correctly classified using the neural model. The forecasting accuracy is significantly better than that obtained with the model that is being used at present by environmental authorities." @default.
- W2219903919 created "2016-06-24" @default.
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- W2219903919 date "2005-04-26" @default.
- W2219903919 modified "2023-09-24" @default.
- W2219903919 title "PM10 Forecasting In Santiago, Chile" @default.
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- W2219903919 doi "https://doi.org/10.2495/air050041" @default.
- W2219903919 hasPublicationYear "2005" @default.
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