Higher-order multivariate markov chains based on particle swarm optimization algorithm for air pollution forecasting

This paper presents a higher-order multivariate Markov chain model combined with particle swarm optimization algorithm. Due to some deficiencies, such as only considering the maximum probability while ignoring the effect of the other probabilities, the traditional method of probability distribution has been replaced by the level characteristics value of fuzzy set theory; further more Particle swarm optimization algorithm has been employed to optimize the coefficient of level characteristics value. In recent years, air pollution acutely aggravates chronic diseases in mankind, such as sulfur dioxide pollution which plays a most important role in acid rain. In order to confront air pollution problems and to plan abatement strategies, both the scientific community and the relevant authorities have focused on monitoring and analyzing the atmospheric pollutants concentration. Taking the forecast of air pollutantsas a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows the new method is predominant in forecasting of multivariate and non-linear data.