Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki

By Jaakko Kukkonen, Leena Partanen, A. Karppinen, J. Ruuskanen, H. Junninen, Mikko T. Kolehmainen, Harri Niska, S. Dorling, Tim J. Chatterton, R. Foxall and 1 more. 2003

349 citations recorded by the source corpus.

Five neural network (NN) models, a linear statistical model and a deterministic modelling system (DET) were evaluated for the prediction of urban NO2 and PM10 concentrations. The model evaluation work considered the sequential hourly concentration time series of NO2 and PM10, which were measured at two stations in central Helsinki, from 1996 to 1999. The models utilised selected traffic flow and pre-processed meteorological variables as input data. An imputed concentration dataset was also created, in which the missing values were replaced, in order to obtain a harmonised database that is well suited for the inter-comparison of models. Three statistical criteria were adopted: the index of agreement (IA), the squared correlation coefficient (R2) and the fractional bias. The results obtained with various non-linear NN models show a good agreement with the measured concentration data for NO2; for instance, the annual mean of the IA values and their standard deviations range from 0.86±0.02 to 0.91±0.01. In the case of NO2, the non-linear NN models produce a range of model performance values that are slightly better than those by the DET. NN models generally perform better than the statistical linear model, for predicting both NO2 and PM10 concentrations. In the case of PM10, the model performance statistics of the NN models were not as good as those for NO2 over the entire range of models considered. However, the currently available NN models are neither applicable for predicting spatial concentration distributions in urban areas, nor for evaluating air pollution abatement scenarios for future years.

Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/ae6a66b0ed059aee698c8f98738c3b425b2900bd

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In Automotive Engineering

1. Said Munir
2. Turki M. Habeebullah
3. Arwa S. Sayegh

In Environmental Engineering

1. Said Munir
2. Turki M. Habeebullah
3. Arwa S. Sayegh

In Health, Toxicology and Mutagenesis

1. Said Munir
2. Turki M. Habeebullah
3. Arwa S. Sayegh

Citations in the corpus

• Comparing the Performance of Statistical Models for Predicting PM10 Concentrations (2014, background): “Despite the extensive research on comparing different modelling techniques (e.g., Kukkonen et al., 2003; Paschalidou et al., 2011; Ul-Saufie et al., 2011), no specific research has been devoted into comparing a number of linear and non-linear models for predicting PM10 levels.”