Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
Open Science Slovenia
Open Science
DiKUL
slv
|
eng
Search
Browse
New in RUL
About RUL
In numbers
Help
Sign in
A machine learning approach to predict air quality in California
ID
Castelli, Mauro
(
Author
),
ID
Martins Clemente, Fabiana
(
Author
),
ID
Popovič, Aleš
(
Author
),
ID
Silva, Sara
(
Author
),
ID
Vanneschi, Leonardo
(
Author
)
PDF - Presentation file,
Download
(7,34 MB)
MD5: D5CE63562DB8E1D18E69F2F60E67D3A4
Image galllery
Abstract
Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data.
Language:
English
Keywords:
informatics
,
ecology
,
artificial intelligence
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
EF - School of Economics and Business
Publication version:
Version of Record
Year:
2020
PID:
20.500.12556/RUL-118023
UDC:
659.2:004
ISSN on article:
1076-2787
DOI:
10.1155/2020/8049504
COBISS.SI-ID:
24544771
Publication date in RUL:
13.08.2020
Views:
1424
Downloads:
654
Metadata:
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Copy citation
Share:
Record is a part of a journal
Title:
Complexity
Shortened title:
Complexity
Publisher:
Wiley & Sons
ISSN:
1076-2787
COBISS.SI-ID:
1926171
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
13.08.2020
Secondary language
Language:
Slovenian
Keywords:
informatika
,
ekologija
,
umetna inteligenca
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
P5-0410
Name:
Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe
Similar documents
Similar works from RUL:
Similar works from other Slovenian collections:
Back