A Novel Hybrid Machine Learning Method (OR-ELM-AR) Used in Forecast of PM<sub>2.5</sub> Concentrations and Its Forecast Performance Evaluation
oleh: Guibin Lu, Enping Yu, Yangjun Wang, Hongli Li, Dongpo Cheng, Ling Huang, Ziyi Liu, Kasemsan Manomaiphiboon, Li Li
| Format: | Article |
|---|---|
| Diterbitkan: | MDPI AG 2021-01-01 |
Deskripsi
Accurate forecast of PM<sub>2.5</sub> pollution is highly needed for the timely prevention of haze pollution in many cities suffered from frequent haze pollution. In this work, an online recurrent extreme learning machine (OR-ELM) technique with online data update was used in the forecast of PM<sub>2.5</sub> pollution for the first time, and a hybrid model (OR-ELM-AR) by combining autoregressive (AR) model was proposed to enhance its forecast ability to capture the variations of hourly PM<sub>2.5</sub> concentration. Evaluation of forecast performances in terms of pollution levels, forecast times, spatial distributions were conducted over the Yangtze River Delta (YRD) region, China. Results indicated that the OR-ELM-AR model could quickly respond to short-term changes and had better forecast performance. Therefore, the OR-ELM-AR model is a promising tool for air pollution forecast of supporting the government to take urgent actions to reduce the frequency and severity of haze pollution in cities or regions.