Title :
Combining classification and regression trees and the neuro-fuzzy inference system for environmental data modeling
Author :
Burrows, William R.
Author_Institution :
Meteorol. Res. Branch, Atmos. Environ. Service, Downsview, Ont., Canada
Abstract :
A procedure is presented for dynamic statistical modeling of predictands with nonlinear predictand-predictor relationships when there are many potential predictors. Classification and regression trees (CART) are used for predictor selection and data stratification. The CART output is suitable for piecewise-continuous predictands. Using predictors selected by CART, a neuro-fuzzy inference system (NFIS) algorithm produces an output model for continuous predictands. An application to modeling ground-level ozone is discussed
Keywords :
air pollution; data models; environmental science computing; forecasting theory; fuzzy neural nets; inference mechanisms; ozone; pattern classification; piecewise polynomial techniques; statistical analysis; trees (mathematics); CART; O3; classification trees; data stratification; dynamic statistical modeling; environmental data modeling; ground-level O3 modelling; neuro-fuzzy inference system; nonlinear predictand-predictor relationships; output model; piecewise-continuous predictands; predictor selection; regression trees; Application software; Atmospheric modeling; Classification tree analysis; Demand forecasting; Inference algorithms; Meteorology; Predictive models; Regression tree analysis; Weather forecasting; Wind forecasting;
Conference_Titel :
Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American
Conference_Location :
New York, NY
Print_ISBN :
0-7803-5211-4
DOI :
10.1109/NAFIPS.1999.781783
