Articles | Volume 14, issue 3
Geosci. Model Dev., 14, 1553–1574, 2021
https://doi.org/10.5194/gmd-14-1553-2021
Geosci. Model Dev., 14, 1553–1574, 2021
https://doi.org/10.5194/gmd-14-1553-2021

Model description paper 17 Mar 2021

Model description paper | 17 Mar 2021

MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series

Lukas Hubert Leufen et al.

Model code and software

MLAir (v1.0.0) - a tool to enable fast and flexible machine learning on air data time series - Source Code Lukas Hubert Leufen, Felix Kleinert, and Martin Georg Schultz https://doi.org/10.34730/5a6c3533512541a79c5c41061743f5e3

MLAir (v1.0.0) - a tool to enable fast and flexible machine learning on air data time series - Docker Image Lukas Hubert Leufen, Felix Kleinert, and Martin Georg Schultz https://doi.org/10.34730/5a6c3533512541a79c5c41061743f5e3

Download
Short summary
MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using machine learning (ML). MLAir is adaptable to a wide range of ML use cases, focusing in particular on deep learning. The user has a free hand with the ML model itself and can select from different methods during preprocessing, training, and postprocessing. MLAir offers tools to track the experiment conduction, documents necessary ML parameters, and creates a variety of publication-ready plots.