Using machine learning for air quality prediction and sustainable urban planning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259457" target="_blank" >RIV/61989100:27230/25:10259457 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2666188825005453?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2666188825005453?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.sftr.2025.100981" target="_blank" >10.1016/j.sftr.2025.100981</a>
Alternative languages
Result language
angličtina
Original language name
Using machine learning for air quality prediction and sustainable urban planning
Original language description
Air pollution has become a very serious issue worldwide, as evidenced by increasing PM2.5 levels. It is not just unhealthy to breathe in that polluted air, but it also causes respiratory problems, heart diseases, and lung cancer. There are many contributors to this environmental issue, including industrial growth, expansion of urban areas, agriculture, and burning of fossil fuels. All these sources emit harmful substances into the atmosphere and deteriorate air quality. Accurate and reliable tracking and prediction of air quality is very important for protecting public health, and it also aligns with Sustainable Development Goal (SDG) of "Good Health and WellBeing", which aims to ensure good health for all. With the latest developments in artificial intelligence, machine learning has emerged as a valuable tool for air quality forecasting. This research investigates an innovative approach to predict the levels of air pollutants in Lahore, Pakistan, using data from January 2003 to December 2022, covering eight pollutants and four weather-related factors. The study uses several time-series models: SARIMA, seasonal autoregressive integrated moving-average with exogenous, Long short-term memory or LSTMS, and non-linear autoregressive. Two different evaluation performance criteria are deployed to evaluate these models: Root mean squared error (RMSE) and DTW for their overall performance metric. Results indicate that NAR has performed better than others with a minimum RMSE of 23.52 and a DTW of 5023. Results indicate a projected % increase in AQI by 13 % for 2030 from the base year 2022. The study provides important information regarding future trends in air quality. It offers different strategies for pollution mitigation that regulators can adopt with the support of strategic planning and policymaking in line with SDGs' objectives.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10510 - Climatic research
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sustainable Futures
ISSN
2666-1888
e-ISSN
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Volume of the periodical
10
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
13
Pages from-to
nestránkováno
UT code for WoS article
001530522000001
EID of the result in the Scopus database
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