Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features
Abstract In this work, we present an Artificial Neural Network for operational forecasting of the SYM‐H geomagnetic index up to 2 hr ahead using the Interplanetary Magnetic Field, the solar wind plasma features and previous SYM‐H values. Former works that forecast the SYM‐H index use data measured b...
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003485 |
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| _version_ | 1841536331836030976 |
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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 work, we present an Artificial Neural Network for operational forecasting of the SYM‐H geomagnetic index up to 2 hr ahead using the Interplanetary Magnetic Field, the solar wind plasma features and previous SYM‐H values. Former works that forecast the SYM‐H index use data measured by ACE, in particular from the MAG and SWEPAM instruments. However, the plasma data present a high amount of missing samples. This issue has been addressed in the literature, often using linear interpolation, which leads to a non‐accurate data reconstruction, specially when the features are missing during the most intense periods of a geomagnetic storm. To overcome that issue, we use ACE's Solar Wind Ion Composition Spectrometer (SWICS) data to fill the missing plasma features. To validate this technique, we compare the results of our forecasting model trained using plasma features in two ways: only using SWEPAM and performing linear interpolation and using SWICS to fill the missing values in SWEPAM. Then, both models are evaluated in an operational scenario, when only SWEPAM data are available and interpolation can only be performed if the missing values are surrounded by valid measurements. In both cases, our model outperforms the current literature forecasting the SYM‐H one and 2 hr ahead, yielding the best results when the training has been done using the data completed using SWICS measurements. |
| format | Article |
| id | doaj-art-63ba2e03315041b4aa6a3f660a718006 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-63ba2e03315041b4aa6a3f660a7180062025-01-14T16:31:19ZengWileySpace Weather1542-73902023-08-01218n/an/a10.1029/2023SW003485Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma FeaturesArmando 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 work, we present an Artificial Neural Network for operational forecasting of the SYM‐H geomagnetic index up to 2 hr ahead using the Interplanetary Magnetic Field, the solar wind plasma features and previous SYM‐H values. Former works that forecast the SYM‐H index use data measured by ACE, in particular from the MAG and SWEPAM instruments. However, the plasma data present a high amount of missing samples. This issue has been addressed in the literature, often using linear interpolation, which leads to a non‐accurate data reconstruction, specially when the features are missing during the most intense periods of a geomagnetic storm. To overcome that issue, we use ACE's Solar Wind Ion Composition Spectrometer (SWICS) data to fill the missing plasma features. To validate this technique, we compare the results of our forecasting model trained using plasma features in two ways: only using SWEPAM and performing linear interpolation and using SWICS to fill the missing values in SWEPAM. Then, both models are evaluated in an operational scenario, when only SWEPAM data are available and interpolation can only be performed if the missing values are surrounded by valid measurements. In both cases, our model outperforms the current literature forecasting the SYM‐H one and 2 hr ahead, yielding the best results when the training has been done using the data completed using SWICS measurements.https://doi.org/10.1029/2023SW003485geomagnetic indices forecastingimputationmachine learningartificial neural networksattentionSYM‐H operational |
| spellingShingle | Armando Collado‐Villaverde Pablo Muñoz Consuelo Cid Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features Space Weather geomagnetic indices forecasting imputation machine learning artificial neural networks attention SYM‐H operational |
| title | Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features |
| title_full | Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features |
| title_fullStr | Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features |
| title_full_unstemmed | Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features |
| title_short | Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features |
| title_sort | neural networks for operational sym h forecasting using attention and swics plasma features |
| topic | geomagnetic indices forecasting imputation machine learning artificial neural networks attention SYM‐H operational |
| url | https://doi.org/10.1029/2023SW003485 |
| work_keys_str_mv | AT armandocolladovillaverde neuralnetworksforoperationalsymhforecastingusingattentionandswicsplasmafeatures AT pablomunoz neuralnetworksforoperationalsymhforecastingusingattentionandswicsplasmafeatures AT consuelocid neuralnetworksforoperationalsymhforecastingusingattentionandswicsplasmafeatures |