MLAir (v1.0.0) - a tool to enable fast and flexible machine learning on air data time series - Source Code

DOI

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.

Identifier
DOI https://doi.org/10.34730/fcc6b509d5394dad8cfdfc6e9fff2bec
Source https://b2share.fz-juelich.de/records/fcc6b509d5394dad8cfdfc6e9fff2bec
Metadata Access https://b2share.fz-juelich.de/api/oai2d?verb=GetRecord&metadataPrefix=eudatcore&identifier=oai:b2share.fz-juelich.de:b2rec/fcc6b509d5394dad8cfdfc6e9fff2bec
Provenance
Creator Leufen, Lukas Hubert; Kleinert, Felix; Schultz, Martin Georg
Publisher EUDAT B2SHARE
Publication Year 2020
Rights The MIT License (MIT); info:eu-repo/semantics/openAccess
OpenAccess true
Contact l.leufen(at)fz-juelich.de
Representation
Language English
Resource Type Software
Format txt; whl; md; zip
Size 2.5 MB; 4 files
Version v1.0.0
Discipline 3.3.14 → Earth sciences → Meteorology; 4.1.17.1.2.1 → Machine learning → Artificial neural network; 4.1.13 → Computer sciences → Software engineering