Air quality modelling through evolutionary computing: A review

• Type: Chapter or Part of a Book
• Date: 2013

Authors

Pires, JCM (Author) FEUP; Martins, FG (Author) FEUP

Book

Title: Air Pollution: Sources, Prevention and Health Effects

Pages: 185-196 Ch. 11

ISBN: 978-1-62417-746-0

Publisher: Nova Science Publishers

Indexing

Scopus - 0 Citations

Scientific classification

CORDIS: Technological sciences > Engineering > Simulation engineering ; Natural sciences > Environmental science

FOS: Engineering and technology > Environmental engineering

Associated Projects

UI0511 - PROJECTO ESTRATÉGICO - UI 511 - 2013-2014

Other information

Authenticus ID: P-008-YM5

Abstract (EN): The air quality models should be able to provide early warnings before the pollution episodes occur, thus contributing for human health and environmental protection. However, the modelling of real-world processes such as air quality is generally a difficult task due to both chaotic and non-linear phenomena and high dimensional sample spaces. Several statistical models were applied to predict air pollutant concentrations, but their performance was always dependent on the time and location of the performed study. Consequently, the models should be optimized concerning their parameters and also their structures. Evolutionary computing encloses two important artificial intelligence strategies: genetic algorithms and genetic programming. Both methodologies use the same principles of the Darwinian Theory of Evolution, enabling the simultaneous optimization of the model structures and parameters. This study aims to present the results of several studies that applied these evolutionary procedures to predict air pollutant concentrations. The studies reported that these methodologies presented good performances and could be used to solve environmental complex problems. They had the ability to select the important variables that mostly influence the concentrations of the studied air pollutants. However, the main disadvantage of these approaches is the high required computation time. In some of these studies, the achieved models presented good performance in prediction of extreme values, which is useful for human health protection, as they can provide more reliable early warnings about high air pollutant concentration episodes.

Language: English

Type (Professor's evaluation): Scientific