PREDICTION OF GROUND-LEVEL OZONE CONCENTRATIONS THROUGH STATISTICAL MODELS

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Pires, JCM (Author) FEUP; Martins, FG (Author) FEUP; Pereira, MC (Author) FEUP; Alvim Ferraz, MCM (Author) Other

Conference proceedings International

Title: IJCCI 2009: PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE

Pages: 551-554

1st International Joint Conference on Computational Intelligence

Funchal, PORTUGAL, OCT 05-07, 2009

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Technological sciences > Engineering

Other information

Authenticus ID: P-003-QHE

Abstract (EN): This study aims to evaluate the performance of three statistical models: (i) multiple linear regression (MLR), (ii) artificial neural network (ANN) and (iii) multi-gene genetic programming (MGP) for predicting the next day hourly average ozone (O(3)) concentrations. O(3) is an important air pollutant that has several negative impacts. Thus, it is important to develop predictive models to prevent the occurrence of air pollution episodes with a time interval enough to take the necessary precautions. The data were collected in an urban site with traffic influences in Oporto Metropolitan Area, Northern Portugal. The air pollutants data (hourly average concentrations of CO, NO, NO(2), NO(x) and O(3)), the meteorological data (hourly averages of temperature, relative humidity and wind speed) and the day of week were used as inputs for the models. ANN models presented better results in the training step. However, with regards to the aim of this study, MGP presented the best predictions of O(3) concentrations (test step). The good performances of the models showed that MGP is a useful tool to public health protection as it can provide more trustful early warnings to the population about O(3) concentrations episodes.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4