Automatic development of deep neural networks for improving numerical meteorological forecast
This paper briefly describes the examples of deep learning applications to scientific and technical problems, as well as the difficulties that may arise with these applications. The paper shows the importance of the automatic development of deep neural networks. The paper verifies the possibility of...
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| Date: | 2024 |
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| Main Authors: | , |
| Format: | Article |
| Language: | Ukrainian |
| Published: |
PROBLEMS IN PROGRAMMING
2024
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| Subjects: | |
| Online Access: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/607 |
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| Journal Title: | Problems in programming |
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Problems in programming| Summary: | This paper briefly describes the examples of deep learning applications to scientific and technical problems, as well as the difficulties that may arise with these applications. The paper shows the importance of the automatic development of deep neural networks. The paper verifies the possibility of the application of the neuroevolutionary approach to the post-processing of the results of meteorological forecasting (2m temperature) obtained using numerical hydrodynamic methods. The results show that in half of the cases, both the rootmean-square error value and the percentage of improved predictions are better (and in some cases much better) for the neuroevolutionary approach than the corresponding values for the manually designed architecture. Thus, neural network models obtained automatically can outperform manually designed models while applied to improving numerical meteorological forecastsPrombles in programming 2024; 1: 57-63 |
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