Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.
It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.
Sun
Gang
G
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA.
Hoff
Steven J
SJ
Zelle
Brian C
BC
Nelson
Minda A
MA
D000397:Air Pollution / Q000706:statistics & numerical data*; D005544:Forecasting; D008962:Models, Theoretical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D013997:Time Factors