A combined forecasting system based on multi-objective optimization and feature extraction strategy for hourly PM2.5 concentration

Abstract

Accurate hourly PM2.5 concentration prediction plays a key role in air quality monitoring and controlling system, especially when severe haze occurs frequently. A PM2.5 hourly prediction system is developed in this paper, based on an advanced data processing strategy, an effective feature selection technology and a novel optimization algorithm. First, the collected original sequence is decomposed into a group of filters with different wave frequencies and each filter is weighted and reconstructed to mitigate the negative impact of noisy fluctuations. Then mRMR is introduced for extracting interaction information between pollutants, further determining the input of artificial intelligence models. Whereafter, a five-component combined system is taken shape, in which BPNN, ELM, GRNN and BiLSTM are employed as foundation models while Multi-objective Water Cycle Algorithm (MOWCA) is the weight optimization model. The results of hourly PM2.5 concentration prediction simulation experiment in the Bei–Shang–Guang–Shen area make clear that the developed system with minimum forecasting error, excellent generalization capability and robust prediction performance shows a definite latent capacity and future to deal with early warning problems and to design suitable abatement strategies.