Local differences in feature importance for the prediction of PM10, O3 and NOx using Dresden as example

Local differences in feature importance for the prediction of PM₁₀, O₃ and NO_x using Dresden as example

Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review

Sophie Teichmann - , Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden) (Author)
Lukas Ninnemann - , Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden) (Author)
Jochen Schanze - , Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden), Leibniz Institute of Ecological Urban and Regional Development (Author)
Jyotirmaya Ijaradar - , Chair of Traffic Process Automation (Author)
Hendrik Herold - , Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Leibniz Institute of Ecological Urban and Regional Development (Author)

Original language	English
Title of host publication	28th AGILE Conference on Geographic Information Science - Poster Track, Dresden, Germany
Number of pages	4
Publication status	Published - 4 May 2025
Peer-reviewed	Yes

Title	28th AGILE Conference on Geographic Information Science
Subtitle	Geographic Information Science responding to Global Challenges
Abbreviated title	AGILE 2025
Conference number	28
Duration	10 - 13 June 2025
Website	https://agile-gi.eu/
Degree of recognition	International event
Location	Deutsches Hygiene-Museum
City	Dresden
Country	Germany