Multilayer Perceptron And Regression
Modelling To Forecast Hourly Nitrogen
Dioxide Concentrations
Author(s)
C. Capilla
Abstract
This paper presents the application of feed-forward multilayer perceptron
networks and multiple regression models, to forecast hourly nitrogen dioxide
levels 24 hours in advance. Input data are traffic and meteorological variables,
and nitrogen dioxide hourly levels. The introduction of four periodic components
(sine and cosine terms for the daily and weekly cycles), and nitrogen oxide
hourly levels was analyzed in order to improve the prediction power. The data
were measured for three years at two monitoring stations in Valencia (Spain).
The model evaluation criteria were the mean absolute error, the root mean square
error and the mean absolute percentage error. The multilayer perceptron
networks performed better than the regression models in nonlinear relationships
like that involving nitrogen oxides, meteorological and traffic variables.
Comparisons of the multilayer perceptron-based models proved that the insertion
of the four additional seasonal input variables improved the ability of obtaining
more accurate predictions, which emphasizes the importance of taking into
account the seasonal character of nitrogen dioxide. The advantages of neural
networks were that they did not require very exhaustive information about air
pollutants, reaction mechanisms, meteorological parameters or traffic flow, and
that they had the ability of allowing nonlinear relationships between very
different predictor variables in an urban environment.
Keywords
air quality, nitrogen dioxide concentration, urban atmosphericpollution, multilayer perceptron, multiple regression model.
Related Book
Other papers in this volume
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