2026-06-22T15:28:47Z https://b2share.fz-juelich.de/api/oai2d

oai:b2share.fz-juelich.de:b2rec/fcc6b509d5394dad8cfdfc6e9fff2bec 2020-10-15T06:49:33Z e9b9792e-79fb-4b07-b6b4-b9c2bd06d095

EUDAT https://b2share.fz-juelich.de/records/fcc6b509d5394dad8cfdfc6e9fff2bec 10.34730/fcc6b509d5394dad8cfdfc6e9fff2bec EUDAT B2SHARE 2020 Leufen, Lukas Hubert Kleinert, Felix Schultz, Martin Georg MLAir (Machine Learning on Air data) is an environment that simplifies and accelerates the creation of new machine learning (ML) models for the analysis and forecasting of meteorological and air quality time series. Current developments can be tracked in the gitlab repository: https://gitlab.version.fz-juelich.de/toar/mlair This resource contains the MLAir version 1.0.0 in a zip archive, as well the requirements, a readme, and distribution file for easy installation using the package installer for python (pip). Instructions on the installation von MLAir can be found in the readme file. 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 workflow python MLAir air quality software time series neural network whl md zip txt l.leufen@fz-juelich.de v1.0.0 2.5 MB 4 files