| Title |
Assesing AI algorithms for predictive modeling of spatiotemporal PM₁₀ air pollution |
| Translation of Title |
DI algoritmų taikymas projektuojamojo erdvės laiko PM₁₀ ir oro taršai modeliuoti. |
| Authors |
Fahim, Mina Adel Shokry ; Sužiedelytė Visockienė, Jūratė ; Grubliauskas, Raimondas |
| DOI |
10.3846/da.2025.017 |
| eISBN |
9786094763908 |
| Full Text |
|
| Is Part of |
Darni aplinka : 28-osios Lietuvos jaunųjų mokslininkų konferencijos „Mokslas - Lietuvos ateitis“ straipsnių rinkinys, 2025 m. balandžio 24 d. = Sustainable environment : proceedings of the 28th conference for junior researchers "Science - future of Lithuania", 24 April 2025, Vilnius, Lithuania.. Vilnius : Vilniaus Gedimino technikos universitetas, 2025. p. 98-104.. ISSN 2029-7157. eISSN 2029-7149. eISBN 9786094763908 |
| Keywords [eng] |
GIS ; machine learning ; meteorological data ; particulate matter ; predictive accuracy ; remote sensing ; Sentinel-5P TROPOMI |
| Abstract [eng] |
Without a doubt, air pollution is one of the most serious issues confronting our world today, which presents significant health and environmental risks, exacerbating respiratory ailments and contributing to climate change. Air pollutants’ spatial and temporal variability is the basis for effective air quality management, necessitating more accurate predictive models. The study aims to assess particulate matter of a diameter smaller than 10 μm (PM₁₀) forecasts using the European Union’s Space Copernicus program mission of monitoring the atmosphere and tracking air pollutants, the Sentinel-5 Precursor satellite (5P) TROPOspheric Monitoring Instrument (TROPOMI), coupled with meteorological variables and observations from air quality monitoring stations. Root mean square error (RMSE) and mean absolute error (MAE) measure the model’s accuracy. The study integrated machine learning algorithms and diverse datasets to enable precise spatial modelling of PM₁₀ concentrations using a geographic information system (GIS). The results obtained peak accuracy during the heating season validation yielded an RMSE of 4.52 μg/m³, MSE of 20.44 (μg/m³)², and MAE of 3.30 μg/m³, while testing resulted in an RMSE of 4.38 μg/m³, MSE of 19.21 (μg/m³)², and MAE of 3.19 μg/m³. |
| Published |
Vilnius : Vilniaus Gedimino technikos universitetas, 2025 |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2025 |
| CC license |
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