A novel structure grey prediction model with strong compatibility and its application in forecasting the annual average concentration of particulate matter in Beijing

Abstract

Beijing has long suffered from the events of fine particulate matter pollution. Consequently, it is of paramount importance to be able to scientifically and reasonably predict the concentration of particulate matter. To this end, a novel prediction model with an adaptive grey generation operator and fractional derivative is proposed. The novel model initially expands the range values for the order of the grey generation operator to enhance data preprocessing capabilities. Meanwhile, the introduction of fractional derivatives enhances the model’s adaptability and overcomes the limitations of traditional grey prediction models that use integer-order differential equations. Subsequently, the fractional derivative and operator orders of the novel model are optimized to find the best parameter combination, thereby enhancing the model's performance. Furthermore, the novel model is compatible with seven other models when its parameters take different values, thus expanding the model’s applicability. The novel model is then applied to simulate and predict the particulate matter concentration in Beijing, achieving a comprehensive mean relative percentage error of only 1.86%, significantly outperforming several similar models(17.56%,17.64%,3.42%). Finally, the paper presents a forecast of the particulate matter concentration in Beijing over the next four years.