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| 001 | 891148 | ||
| 005 | 20230127125338.0 | ||
| 024 | 7 | _ | |a 10.5194/gmd-14-1553-2021 |2 doi |
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| 037 | _ | _ | |a FZJ-2021-01397 |
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| 100 | 1 | _ | |a Leufen, Lukas Hubert |0 P:(DE-Juel1)177004 |b 0 |e Corresponding author |
| 245 | _ | _ | |a MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2021 |b Copernicus |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a With MLAir (Machine Learning on Air data) we created a software environment that simplifies and accelerates the exploration of new machine learning (ML) models, specifically shallow and deep neural networks, for the analysis and forecasting of meteorological and air quality time series. Thereby MLAir is not developed as an abstract workflow, but hand in hand with actual scientific questions. It thus addresses scientists with either a meteorological or an ML background. Due to their relative ease of use and spectacular results in other application areas, neural networks and other ML methods are also gaining enormous momentum in the weather and air quality research communities. Even though there are already many books and tutorials describing how to conduct an ML experiment, there are many stumbling blocks for a newcomer. In contrast, people familiar with ML concepts and technology often have difficulties understanding the nature of atmospheric data. With MLAir we have addressed a number of these pitfalls so that it becomes easier for scientists of both domains to rapidly start off their ML application. MLAir has been developed in such a way that it is easy to use and is designed from the very beginning as a stand-alone, fully functional experiment. Due to its flexible, modular code base, code modifications are easy and personal experiment schedules can be quickly derived. The package also includes a set of validation tools to facilitate the evaluation of ML results using standard meteorological statistics. MLAir can easily be ported onto different computing environments from desktop workstations to high-end supercomputers with or without graphics processing units (GPUs). |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a IntelliAQ - Artificial Intelligence for Air Quality (787576) |0 G:(EU-Grant)787576 |c 787576 |f ERC-2017-ADG |x 1 |
| 536 | _ | _ | |a Deep Learning for Air Quality and Climate Forecasts (deepacf_20191101) |0 G:(DE-Juel1)deepacf_20191101 |c deepacf_20191101 |f Deep Learning for Air Quality and Climate Forecasts |x 2 |
| 536 | _ | _ | |0 G:(DE-Juel-1)ESDE |a Earth System Data Exploration (ESDE) |c ESDE |x 3 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Kleinert, Felix |0 P:(DE-Juel1)176602 |b 1 |
| 700 | 1 | _ | |a Schultz, Martin G. |0 P:(DE-Juel1)6952 |b 2 |
| 773 | _ | _ | |a 10.5194/gmd-14-1553-2021 |g Vol. 14, no. 3, p. 1553 - 1574 |0 PERI:(DE-600)2456725-5 |n 3 |p 1553 - 1574 |t Geoscientific model development |v 14 |y 2021 |x 1991-9603 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/891148/files/invoice_Helmholtz-PUC-2021-21.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/891148/files/MLAir%20%E2%80%93%20a%20tool%20to%20enable%20fast%20and%20flexible%20machine%20learning%20on%20air%20data%20time%20series.pdf |y OpenAccess |
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