Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations
By S. Abdul-Wahab, C. Bakheit, S. M. Al-Alawi. Environmental Modelling & Software, 2005
508 citations recorded by the source corpus.
Subfields: Environmental Engineering, Health, Toxicology and Mutagenesis, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/ad67d24eb291b039dfcbfc6b7e39cc6a8e016ff0
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In Environmental Engineering
In Health, Toxicology and Mutagenesis
In Industrial and Manufacturing Engineering
Citations in the corpus
- A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2.5 (2020, methodology): “These PCs are used as input for the GWR model.”
- Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations (2007, methodology): “…as well as principal component regressions (combining multiple linear regressions and principal component analysis) are being used to model ozone concentrations (Schlink et al., in press; Abdul-Wahab et al., 2005; Gonçalves et al., 2005; Lengyel et al., 2004; Abdul-Wahab and Al-Alawi, 2002).”
- Prediction of the Daily Mean PM10 Concentrations Using Linear Models (2008, background): “The PC are ordered in such a way that the first component has the largest fraction of the original data variability [ 11-13 ]”
- Assessing and Modelling the Influence of Household Characteristics on Per Capita Water Consumption (2016, methodology): “1 Models Based on Multiple Linear Regression (STEPWISE) Multiple linear regression technique has been used widely to explore the relationship between the dependent and several independent variables (Abdul-Wahab et al. 2005).”