The early-warning system based on hybrid optimization algorithm and fuzzy synthetic evaluation model

Portending and mastering the atmospheric quality plays a significant role in polishing up people's daily life and preventing the environment against serious pollution because air pollution has exceeded the international warning level. Since, accurate contaminants concentration forecasting and objective evaluation, which is urgent to establish an early-warning system, is a critical issue for the social community. An early-warning framework based on two modules composed of forecasting part and assessment part is proposed and successfully adopted in this paper. The forecasting part is applied to a hybrid optimization model with improved Harmony Search Algorithm with PSO strategy to forecast air contaminants concentration, while the assessment part is applied to Fuzzy Synthetic Evaluation Model with entropy weight to evaluate the air quality levels. The proposed early-warning system was investigated in three cities of Jing-Jin-Ji region of China where there exists serious air pollution for the period August 1st 2015 to September 29st 2016. The findings showed that the forecasting model is greatly superior to statistical model and other models on the urban pollutant concentration data. According to the results of air quality assessment, the evaluation model based on the entropy technique can objectively assess the atmospheric quality level.