Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US)

Authors:
E. Salazar-Ruiz;J. B. Ordieres;E. P. Vergara;S. F. Capuz-Rizo
Affiliations:
Instituto Tecnológico de Mexicali, Av. Tecnológico, s/n Col. Elías Calles, 21396 Mexicali, B.C., Mexico;Universidad de La Rioja, Edificio Departamental, c/Luis de Ulloa 20, E-26004 Logroño, La Rioja, Spain;Universidad de La Rioja, Edificio Departamental, c/Luis de Ulloa 20, E-26004 Logroño, La Rioja, Spain;Universidad Politécnica de Valencia, Camino de Vera, s/n E-46022 Valencia, Spain
Venue:
Environmental Modelling & Software
Year:
2008

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Abstract

This study developed 12 prediction models using two types of data matrix (daily means and a selection of the mean for the first 6h of the day). The Persistence parametric prediction technique was applied separately to these matrices, as well as semiparametric Ridge Regression and three non-parametric or artificial intelligence techniques: Support Vector Machine, Multilayer Perceptron and ELMAN networks. The target was the prediction of maximum tropospheric ozone concentrations for the next day in the Mexicali-Calexico border area. The main ozone precursors and meteorological parameters were used for the different models. The proposals were evaluated using specific performance measurements for the air quality models established in the Model Validation Kit and recommended by the US Environmental Protection Agency. Results with similar margins of error were obtained in various models developed in this study, and some of them have provided smaller margins of error than similar prediction models existing in the literature developed in other regions. For this reason, we consider it feasible to apply the prediction models developed and they could be useful for supporting decisions in the matter of ozone pollution in the region under study, as well as for use in daily forecasting in this area.