Hybrid algorithms based on historical accuracy for forecasting particulate matter concentrations

Abstract

Air pollution has become one of the most significant problems impacting human health. Particulate matter (PM) 2.5 is usually used as an identifier of the intensity of the pollution. The PM2.5 forecasting is essential and gainful for reducing health risks. The efficient model for forecasting PM2.5 concentration can be used in determining the period of outdoor activities, thereby reducing the impact on health. In addition, the government sector can use the forecasting model as a tool for laying down measures a burning control. In this work, the hybrid forecasting algorithms for improving accuracy are presented. The hybrid forecasting algorithms combine neural network models with historical predictive data for improving the accuracy of forecasting. The experimental results show that the proposed algorithms can reduce the mean absolute error and root mean square error of forecasting at 36% and 45%. Therefore, the proposed algorithms are not only effectively used to forecast the PM2.5 concentrations but also apply the lightweight technique based on historical accuracy to forecast other complex problems efficiently.