A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: an artificial intelligent system for mapping environmental pollution.
Adeola A. AkinpeluMd. Eaqub AliTaoreed Olakunle OwolabiMohd Rafie JohanR. SaidurSunday Olusanya OlatunjiZaira ChowdburyPublished in: Neural Comput. Appl. (2020)
Keyphrases
- regression model
- intelligent systems
- prediction model
- prediction intervals
- support vector
- linear regression model
- support vector regression
- computational intelligence
- artificial intelligence
- multiple linear regression
- target variable
- survival analysis
- air pollution
- regression methods
- soft computing
- model selection
- ambient intelligence
- natural environment
- kernel regression
- linear model
- regression analysis
- kernel function
- expert systems
- gaussian process
- generalized linear models
- explanatory variables
- air quality
- multivariate regression
- prediction error
- logistic regression
- support vector machine
- feature selection
- predictive model
- ls svm
- semi parametric
- regression trees
- software reliability
- linear regression
- locally weighted
- fuzzy logic
- decision making