A neural network based model to forecast hourly ozone levels in rural areas in the Basque Country

• Published in: AIR POLLUTION XV Vol. I; pp. 109 - 118
• Main Authors: AGIRRE, E, ANTA, A, BARRON, L. J. R, ALBIZU, M
• Format: Conference Proceeding Journal Article
• Language: English
• Published: Southampton WIT 01.01.2007; W I T Press
• Subjects: Algorithms; Applied sciences; Artificial neural networks; Atmospheric pollution; Basque people; Exact sciences and technology; Mathematical models; Multilayer perceptrons; Neural networks; Nitrogen dioxide; Ozone; Pollutants physicochemistry study: properties, effects, reactions, transport and distribution; Pollution; Pollution monitoring; Rural areas; Stations; Statistical analysis; Europe; Spain; Atmospheric condition; Troposphere; Ozone; Air quality; Neural network; Forecast model; Algorithm; Modeling; Real time system; Atmospheric pollution forecasting; Multilayer perceptrons; Rural area; Air pollution
• ISBN: 9781845640675; 1845640675
• ISSN: 1746-448X; 1743-3541
• DOI: 10.2495/AIR070111

The goal of this work is to build and evaluate a multilayer perceptron based model to forecast tropospheric ozone (O3) levels, in real-time, up to eight hours ahead at two rural stations located in the Autonomous Community of the Basque Country (North Central Spain). Current and historical hourly concentrations of ozone, nitrogen dioxide (NO2) and meteorological variables were used to determine the input variables of the model. The designed basic model established sixteen multilayer perceptrons, which were trained using the scaled conjugate gradient algorithm. The performance of the model was evaluated using the statistics of the Model Validation Kit. The study proved the capability of artificial neural networks to forecast efficiently ozone concentrations at rural stations in the Basque Country.