Research of a novel combined deterministic and probabilistic forecasting system for air pollutant concentration

Hazy weather seriously threatens human health, providing accurate and reliable air pollutant concentration forecast will be of great scientific value and benefit to residents' livelihood and natural environment. However, existing forecasting models lack outlier detection, feature mapping and uncertainty forecast, which results in unreliable forecasting results. Thus, in this paper, a novel combined deterministic and probabilistic forecasting system based on meta-heuristic algorithm is proposed, which contains multiple data pre-processing, combined deterministic and probabilistic forecast. Firstly, the raw data is pre-processed and analyzed from multiple perspectives to fully extract the chaotic features. Then multi-objective white shark optimizer is utilized to combine the three deep learning models to enhance the forecasting accuracy and stability. Furthermore, the Pin-ball loss function is employed to yield prediction intervals at different confidence levels, and a novel interval pseudo-code is constructed to optimize the prediction intervals thus promoting the probabilistic forecasting reliability. The simulations indicated that the developed system enhances the forecasting accuracy by maximum 60.6772%, 54.9793% and 38.6876% in the three steps of PM2.5 prediction and up to 64.9001%, 68.4637% in the interval sharpness at significance level 0.05 and 0.1 than the tested models.