Prediction of PM2.5 concentration based on multi-source data and self-organizing fuzzy neural network

The fine particulate matter (PM2.5) problem has become one of the biggest topics of concern in many cities, such as Beijing in China. This study attempts to employ a self-organizing fuzzy neural network (SOFNN) to predict the hourly concentration of PM2.5 using multi-source data. The multi-source data including meteorological data, pollutant concentration data and image data are first obtained through different methods. Then, a novel SOFNN, which is optimized by an improved gradient descent algorithm, is proposed based on the mutual information (MI) and sensitivity analysis (SA). More importantly, MI and SA are introduced to effectively adjust the structure of fuzzy neural network without changing the performance of the original network. Finally, the MI- and SA-based SOFNN is deployed to infer PM2.5 concentration at the study area. The experimental results show that our proposed model can achieve a more compact network structure, higher prediction performance and lower computation time as compared with state-of-the-art or popular methods.