Prediction of daily maximum ground ozone concentration using support vector machine

Abstract

The accurate predictions of ground ozone concentrations are required for proper management, control, and making public warning strategies. Due to the difficulties in handling phenomenological models that are based on complex chemical reactions of ozone production, neural network models gained popularity in the last decade. These models also have some limitations due to problems of overfitting, local minima, and tuning of network parameters. In this study, the predictions of daily maximum ozone concentrations are attempted using support vector machines (SVMs). The comparison between the accuracy of SVM and neural network predictions is performed to evaluate their performance. For this, the daily maximum ozone concentration data observed during 2002-2004 at a site in Delhi is utilized. The models are developed using the available meteorological parameters. The results indicated the promising performance of SVM over neural networks in predicting daily maximum ozone concentrations.