Multi-swarm coevolution real-time data forecasting model used in atmospheric environmental monitoring

With the help of powerful function mapping capability of Multi-swarm Coevolution work, this paper presents a Multi-swarm Coevolution real-time data forecasting model which is suitable for the atmospheric environmental monitoring by using the correlation between the temperature, humidity and humidity. The model is based on the size of the correlation coefficient to identify the weights of relative factors. The functional relationship of the temperature, humidity and humidity can be mapped more accurately. Then the trends of humidity could be predicted accurately. By comparing the unimproved Multi-swarm Coevolution work algorithm, it is proved that the model has high prediction accuracy. The improved Multi-swarm Coevolution real-time data forecasting model is applied to PM2.5 value prediction. This model can be applied to atmospheric environmental monitoring in real-time data forecasting.