Optimal Modeling of Urban Ambient Air Ozone Concentration Based on Its Precursors' Concentrations and Temperature, Employing Genetic Programming and Genetic Algorithm.

Efficient models are required to predict the optimum values of ozone concentration in different levels of its precursors' concentrations and temperatures. A novel model based on the application of a genetic programming (GP) optimization is presented in this article. Ozone precursors' concentrations and run time average temperature have been chosen as model's parameters. Generalization performances of two different homemade models based on genetic programming and genetic algorithm (GA), which can be used for calculating theoretical ozone concentration, are compared with conventional semi-empirical model performance. Experimental data of Mashhad city ambient air have been employed to investigate the prediction ability of properly trained GP, GA, and conventional semi-empirical models. It is clearly demonstrated that the in-house algorithm which is used for the model based on GP, provides better generalization performance over the model optimized with GA and the conventional semi-empirical ones. The proposed model is found accurate enough and can be used for urban air ozone concentration prediction.