A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network

Article ID	Journal	Published Year	Pages	File Type
6329448	Science of The Total Environment	2014	11 Pages	PDF

Keywords

ANN AQI EEMD MAPE ARIMA GRNN MLR RMSE EMD PM2.5 daily minimum temperature MAE Empirical mode decomposition Ensemble Empirical mode decomposition Intrinsic mode functions daily maximum temperature Principal components regression Multiple linear regression Root mean square error index of agreement Air Quality Index General Regression Neural Network artificial neural networks IMFs Mean Absolute Error mean absolute percentage error Autoregressive Integrated Moving Average Mint PCR

Related Topics

Life Sciences Environmental Science Environmental Chemistry

A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network

Authors

Qingping Zhou, Haiyan Jiang, Jianzhou Wang, Jianling Zhou,