Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting

Introduction: This study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O3) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons. Methods: Two of the methodologies define threshold models, which assume that the behaviour of the dependent variable (O3 concentrations) changes when it enters in a different regime (two and four regimes were considered in this study). The change from one regime to another depends on a specific value (threshold value) of an explanatory variable (threshold variable), which is also defined by GAs. The predictor variables were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide, nitrogen dioxide (NO2), and O3 (recorded in the previous day at an urban site with traffic influence) and also meteorological data (hourly averages of temperature, solar radiation, relative humidity and wind speed). The study was performed for the period from May to August 2004. Results and discussion: Several models were achieved and only the best model of each methodology was analysed. In threshold models, the variables selected by GAs to define the O3 regimes were temperature, CO and NO2 concentrations, due to their importance in O3 chemistry in an urban atmosphere. Conclusion: In the prediction of O3 concentrations, the threshold model that considers two regimes was the one that fitted the data most efficiently.