Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

2026-06-14T03:37:05Z https://b2share.fz-juelich.de/api/oai2d

oai:b2share.fz-juelich.de:b2rec/af084443e1c444feb12d83a93a65fa33 2022-02-24T21:39:40Z e9b9792e-79fb-4b07-b6b4-b9c2bd06d095

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties - Source Code EUDAT https://b2share.fz-juelich.de/records/af084443e1c444feb12d83a93a65fa33 10.34730/af084443e1c444feb12d83a93a65fa33 EUDAT B2SHARE 2022 Betancourt, Clara Stomberg, Timo Edrich, Ann-Kathrin Patnala, Ankit Stadtler, Scarlet This source code contains all methods that is being used in ozone mapping project. In addition, it contains scripts to run both explainable AI methods and methods used to study the impact of uncertainties. Current developments can be tracked in the gitlab repository https://gitlab.jsc.fz-juelich.de/esde/machine-learning/ozone-mapping This resource contains our complete source code in a zip archive(ozone-mapping.zip), a readme file (README.md) and setup directory(ozone-mapping-setup.zip) which contains requirements file to run in own system or on our cluster. The MIT License (MIT) info:eu-repo/semantics/openAccess eng Software 3.3.14 → Earth sciences → Meteorology 4.1.17.1.2.1 → Machine learning → Artificial neural network 4.1.13 → Computer sciences → Software engineering machine learning, ozone mapping, neural network, random forest, ozone interpolation, air quality, explainable AI, XAI, area of applicability md zip c.betancourt@fz-juelich.de 11.2 MB 3 files