Air quality prediction algorithm based on improved particle swarm optimization SVM

The invention relates to an air quality prediction algorithm based on an improved particle swarm optimization SVM, and belongs to the field of data mining and evolutionary algorithms. According to thealgorithm, firstly, various meteorological factors such as temperature, humidity, sunlight and precipitation are considered; meteorological elements having strong correlation with air quality are selected to form feature vectors. Then, the feature vectors and air quality data are used as input of an SVM prediction model; an improved particle swarm optimization algorithm is provided for overcomingthe defects that existing conventional particle swarm optimization algorithm is low in convergence speed and prone to falling into a local optimal solution, SVM model parameters are optimized throughthe improved particle swarm optimization algorithm to find out optimal parameters, and finally the air quality is predicted through an optimal parameter model. The improved particle swarm algorithm is utilized to fully mine th