Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks
Abstract In this study, we develop a robust real‐time forecast system for the SYM‐H index using Deep Neural Networks and real‐time Solar Wind measurements along with Interplanetary Magnetic Field parameters. This system provides not only one‐off forecasts but also quantile‐based confidence intervals...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW004039 |
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| _version_ | 1841536365314965504 |
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| author | Armando Collado‐Villaverde Pablo Muñoz Consuelo Cid |
| author_facet | Armando Collado‐Villaverde Pablo Muñoz Consuelo Cid |
| author_sort | Armando Collado‐Villaverde |
| collection | DOAJ |
| description | Abstract In this study, we develop a robust real‐time forecast system for the SYM‐H index using Deep Neural Networks and real‐time Solar Wind measurements along with Interplanetary Magnetic Field parameters. This system provides not only one‐off forecasts but also quantile‐based confidence intervals, offering a range within which the observed SYM‐H values are expected to fall, enhancing forecast reliability and usability. The model is tested both on historical level 2 science‐ready data and on preliminary observations, which are closer to the operational environment that the model is expected to work on, demonstrating the model's robustness and practical utility in real‐time scenarios. The integration of quantile forecasts into SYM‐H prediction models represents a significant advancement, providing decision‐makers with more accurate and trustworthy information to manage the hazard of geomagnetic storms. |
| format | Article |
| id | doaj-art-d916174d3d854b30bd8319c1c89cc23a |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-d916174d3d854b30bd8319c1c89cc23a2025-01-14T16:31:08ZengWileySpace Weather1542-73902024-10-012210n/an/a10.1029/2024SW004039Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural NetworksArmando Collado‐Villaverde0Pablo Muñoz1Consuelo Cid2Department of Computer Engineering Universidad de Alcalá Madrid SpainDepartment of Computer Engineering Universidad de Alcalá Madrid SpainDepartment of Physics and Mathematics Universidad de Alcalá Madrid SpainAbstract In this study, we develop a robust real‐time forecast system for the SYM‐H index using Deep Neural Networks and real‐time Solar Wind measurements along with Interplanetary Magnetic Field parameters. This system provides not only one‐off forecasts but also quantile‐based confidence intervals, offering a range within which the observed SYM‐H values are expected to fall, enhancing forecast reliability and usability. The model is tested both on historical level 2 science‐ready data and on preliminary observations, which are closer to the operational environment that the model is expected to work on, demonstrating the model's robustness and practical utility in real‐time scenarios. The integration of quantile forecasts into SYM‐H prediction models represents a significant advancement, providing decision‐makers with more accurate and trustworthy information to manage the hazard of geomagnetic storms.https://doi.org/10.1029/2024SW004039machine learningSYM‐Hforecastingprediction intervaloperational |
| spellingShingle | Armando Collado‐Villaverde Pablo Muñoz Consuelo Cid Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks Space Weather machine learning SYM‐H forecasting prediction interval operational |
| title | Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks |
| title_full | Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks |
| title_fullStr | Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks |
| title_full_unstemmed | Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks |
| title_short | Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks |
| title_sort | operational sym h forecasting with confidence intervals using deep neural networks |
| topic | machine learning SYM‐H forecasting prediction interval operational |
| url | https://doi.org/10.1029/2024SW004039 |
| work_keys_str_mv | AT armandocolladovillaverde operationalsymhforecastingwithconfidenceintervalsusingdeepneuralnetworks AT pablomunoz operationalsymhforecastingwithconfidenceintervalsusingdeepneuralnetworks AT consuelocid operationalsymhforecastingwithconfidenceintervalsusingdeepneuralnetworks |