Urban Air Quality Analysis and Forecast Based on Intelligent Algorithm with Parameter Optimization and Decision Rules.

Featured Application: Air pollution has become an unavoidable reality in today's world. With the rapid development of various industries and motorized transportation, large amounts of harmful substances such as soot, sulfur dioxides, nitrogen oxides, carbon monoxides, and hydrocarbons are released into the atmosphere, lasting a long time and in concentrations exceeding tolerable environmental limits. In this study, we investigated an intelligent algorithm that had the functions of parameter optimization and decision rules, which we applied to Beijing air quality data to analyze and forecast urban air quality. Air pollution has an ongoing devastating impact on the planet, damaging ecosystems, depleting natural resources, and endangering human health. This paper proposes a new intelligent algorithm that includes parameter optimization and decision rules to forecast and analyze of urban air quality. Through analysis of 24-h daily air quality data provided by the Beijing Air Quality Monitoring Station, simulated annealing (SA) and a decision tree (DT) emerge as the key factors. We prove that in the investigated algorithm, SA and DT can be used to make decision rules and achieve better accuracy for classification. We find that SA can be used to adjust the best parameter settings for the DT. Simulation results show that the accuracy of the proposed algorithm for classification is far better than other existing approaches. [ABSTRACT FROM AUTHOR]