Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations

By S.I.V. Sousa, F. G. Martins, M. Alvim-Ferraz, M. C. Pereira. Environmental Modelling & Software, 2007

522 citations recorded by the source corpus.

Subfields: Environmental Engineering, Health, Toxicology and Mutagenesis, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/b521a77b56b7a9aaf47b897098f17fcac2aa17d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. John P. Norton
2. Anthony J. Jakeman
3. Serena H. Chen

Citations in the corpus

• Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, background): “…also called regression, generates a function from a given set of training patterns, e.g. modelling river sediment yield [33] or catchment water supply [79], predicting ozone concentration [147], modelling leachate flow rate [88] or estimating the nitrate distribution in groundwater [8] .”