Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting

被引：0

作者：

McKendry, Ian G. ^{[1
]}

机构：

[1] Department of Geography, 250-1984 West Mall, Vancouver, BC V6T 1Z2, Canada

来源：

Journal of the Air and Waste Management Association | 2002年 / 52卷 / 09期

关键词：

Forecasting - Meteorology - Neural networks - Ozone - Particles (particulate matter) - Regression analysis;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Multi-layer perceptron (MLP) artificial neural network (ANN) models are compared with traditional multiple regression (MLR) models for daily maximum and average O3 and particulate matter (PM10 and PM2.5) forecasting. MLP particulate forecasting models show little if any improvement over MLR models and exhibit less skill than do O3 forecasting models. Meteorological variables (precipitation, wind, and temperature), persistence, and co-pollutant data are shown to be useful PM predictors. If MLP approaches are adopted for PM forecasting, training methods that improve extreme value prediction are recommended.

引用

页码：1096 / 1101