Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks
Abstract The Disturbance storm time (Dst) index has been widely used as a proxy for the ring current intensity, and therefore as a measure of geomagnetic activity. It is derived by measurements from four ground magnetometers in the geomagnetic equatorial region. We present a new model for predicting...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
|
| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022SW003286 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841536473439928320 |
|---|---|
| author | A. Hu E. Camporeale B. Swiger |
| author_facet | A. Hu E. Camporeale B. Swiger |
| author_sort | A. Hu |
| collection | DOAJ |
| description | Abstract The Disturbance storm time (Dst) index has been widely used as a proxy for the ring current intensity, and therefore as a measure of geomagnetic activity. It is derived by measurements from four ground magnetometers in the geomagnetic equatorial region. We present a new model for predicting Dst with a lead time between 1 and 6 hr. The model is first developed using a Gated Recurrent Unit (GRU) network that is trained using solar wind parameters. The uncertainty of the Dst model is then estimated by using the Accurate and Reliable Uncertainty Estimate method (Camporeale & Carè, 2021, https://doi.org/10.1615/int.j.uncertaintyquantification.2021034623). Finally, a multi‐fidelity boosting method is developed in order to enhance the accuracy of the model and reduce its associated uncertainty. It is shown that the developed model can predict Dst 6 hr ahead with a root‐mean‐square‐error of 13.54 nT. This is significantly better than a persistence model or a single GRU model. |
| format | Article |
| id | doaj-art-3cb36b81a5e0426399ade72233febb3a |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-3cb36b81a5e0426399ade72233febb3a2025-01-14T16:26:47ZengWileySpace Weather1542-73902023-04-01214n/an/a10.1029/2022SW003286Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural NetworksA. Hu0E. Camporeale1B. Swiger2CIRES University of Colorado Boulder CO USACIRES University of Colorado Boulder CO USACIRES University of Colorado Boulder CO USAAbstract The Disturbance storm time (Dst) index has been widely used as a proxy for the ring current intensity, and therefore as a measure of geomagnetic activity. It is derived by measurements from four ground magnetometers in the geomagnetic equatorial region. We present a new model for predicting Dst with a lead time between 1 and 6 hr. The model is first developed using a Gated Recurrent Unit (GRU) network that is trained using solar wind parameters. The uncertainty of the Dst model is then estimated by using the Accurate and Reliable Uncertainty Estimate method (Camporeale & Carè, 2021, https://doi.org/10.1615/int.j.uncertaintyquantification.2021034623). Finally, a multi‐fidelity boosting method is developed in order to enhance the accuracy of the model and reduce its associated uncertainty. It is shown that the developed model can predict Dst 6 hr ahead with a root‐mean‐square‐error of 13.54 nT. This is significantly better than a persistence model or a single GRU model.https://doi.org/10.1029/2022SW003286machine learninguncertainty quantificationgeomagnetic stormspace weathersolar windensemble model |
| spellingShingle | A. Hu E. Camporeale B. Swiger Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks Space Weather machine learning uncertainty quantification geomagnetic storm space weather solar wind ensemble model |
| title | Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks |
| title_full | Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks |
| title_fullStr | Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks |
| title_full_unstemmed | Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks |
| title_short | Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks |
| title_sort | multi hour ahead dst index prediction using multi fidelity boosted neural networks |
| topic | machine learning uncertainty quantification geomagnetic storm space weather solar wind ensemble model |
| url | https://doi.org/10.1029/2022SW003286 |
| work_keys_str_mv | AT ahu multihouraheaddstindexpredictionusingmultifidelityboostedneuralnetworks AT ecamporeale multihouraheaddstindexpredictionusingmultifidelityboostedneuralnetworks AT bswiger multihouraheaddstindexpredictionusingmultifidelityboostedneuralnetworks |