Revisiting urban air quality forecasting: a regression approach

We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, acco...

Full description

Saved in:
Bibliographic Details
Published in:Vietnam journal of computer science 2018-05, Vol.5 (2), p.177-184
Main Authors: Karatzas, Kostas, Katsifarakis, Nikos, Orlowski, Cezary, Sarzyński, Arkadiusz
Format: Article
Language:English
Subjects:
Air pollution
Air quality
Artificial Intelligence
Artificial neural networks
Computational Intelligence
Computer Applications
Computer Science
Computer Systems Organization and Communication Networks
Correlation analysis
Correlation coefficients
e-Commerce/e-business
Ensemble
Forecasting
Forest management
Information Systems and Communication Service
Neural networks
Outdoor air quality
Regression analysis
Regression models
Regular Paper
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, accompanied by Random Forest models, for five locations per study area and for a dataset of limited feature dimensionality. An ensemble approach is also used for generating and testing AQ forecasting models. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the daily mean PM 10 concentration one day in advance reaching 0.765 for one of the TMA locations and 0.64 for one of the GMA locations. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts on the basis of limited feature space dimensionality and by employing simple linear regression models.
ISSN:2196-8888
2196-8896
DOI:10.1007/s40595-018-0113-0