Forecasting PM2.5 Concentration Using Spatio-Temporal Extreme Learning Machine
Authors:
- Bo LiuBLBo Liu
- Shuo YanSYShuo Yan
- Jianqiang LiJLJianqiang Li
- Yong LiYLYong Li
- 1 December 2016
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 950-953
Abstract
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.Keywords
This publication has 5 references indexed in Scilit:
- Air quality prediction using optimal neural networks with stochastic variablesAtmospheric Environment, 2013
- Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component AnalysisPure and Applied Geophysics, 2012
- A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of TunisiaAdvanced Materials Research, 2012
- Prediction of dust concentration in open cast coal mine using artificial neural networkAtmospheric Pollution Research, 2012
- Extreme learning machine: Theory and applicationsNeurocomputing, 2006