Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting.
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.
McKendry
Ian G
IG
Department of Geography, The University of British Columbia, Vancouver, Canada. ian@geog.ubc.ca
D000393:Air Pollutants / Q000032:analysis*; D005544:Forecasting; D008685:Meteorological Concepts; D016571:Neural Networks, Computer; D010316:Particle Size
D000393:Air Pollutants