Forecasting PM2.5 Concentration Using Spatio Temporal Extreme Learning Machine
In recent years, air quality has become a severe environmental problem in China. Since bad air quality brought significant influences on traffic and people's daily life, how to predict the future air quality precisely and subtly, has been an urgent and important problem. In this paper, a Spatio Temporal Extreme Learning Machine (STELM) method is proposed for air quality prediction. STELM considers temporal and spatial characteristics of air quality data and related meteorological data, constructs a prediction model based on ELM, and realizes air quality prediction with more than 80% precision. A prototype system is implemented and the experiments on practical air quality data in Beijing validate the effectiveness of our method and system.
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)