Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach
During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents (GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short T...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Astronomy and Space Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspas.2022.846291/full |
| _version_ | 1818140863951273984 |
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| author | Matthew Blandin Hyunju K. Connor Hyunju K. Connor Doğacan S. Öztürk Amy M. Keesee Victor Pinto Md Shaad Mahmud Chigomezyo Ngwira Shishir Priyadarshi |
| author_facet | Matthew Blandin Hyunju K. Connor Hyunju K. Connor Doğacan S. Öztürk Amy M. Keesee Victor Pinto Md Shaad Mahmud Chigomezyo Ngwira Shishir Priyadarshi |
| author_sort | Matthew Blandin |
| collection | DOAJ |
| description | During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents (GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|BN|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting |BN|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |BN|. The neural network models show significant increases over the linear regression models for |BN| thresholds. We also calculate the Heidke skill scores for d|BN|/dt by deriving d|BN|/dt from |BN| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict BN instead of |BN|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times. |
| first_indexed | 2024-12-11T10:50:45Z |
| format | Article |
| id | doaj.art-409d9ad3f5b84210b6e226869252b284 |
| institution | Directory Open Access Journal |
| issn | 2296-987X |
| language | English |
| last_indexed | 2024-12-11T10:50:45Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj.art-409d9ad3f5b84210b6e226869252b2842022-12-22T01:10:19ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2022-05-01910.3389/fspas.2022.846291846291Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model ApproachMatthew Blandin0Hyunju K. Connor1Hyunju K. Connor2Doğacan S. Öztürk3Amy M. Keesee4Victor Pinto5Md Shaad Mahmud6Chigomezyo Ngwira7Shishir Priyadarshi8Department of Physics and Geophysical Institute, University of Alaska, Fairbanks, AK, United StatesDepartment of Physics and Geophysical Institute, University of Alaska, Fairbanks, AK, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesDepartment of Physics and Geophysical Institute, University of Alaska, Fairbanks, AK, United StatesDepartment of Physics and Astronomy and Space Science Center, University of New Hampshire, Durham, NH, United StatesDepartment of Physics and Astronomy and Space Science Center, University of New Hampshire, Durham, NH, United StatesDepartment of Electrical and Computer Engineering, University of New Hampshire, Durham, NH, United StatesASTRA, Boulder, CO, United StatesDepartment of Electronics and Electrical Engineering, University of Bath, Bath, United KingdomDuring periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents (GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|BN|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting |BN|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |BN|. The neural network models show significant increases over the linear regression models for |BN| thresholds. We also calculate the Heidke skill scores for d|BN|/dt by deriving d|BN|/dt from |BN| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict BN instead of |BN|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.https://www.frontiersin.org/articles/10.3389/fspas.2022.846291/fullspace weatherGICgeomagnetic stormsground geomagnetic fieldmachine learningneural networks |
| spellingShingle | Matthew Blandin Hyunju K. Connor Hyunju K. Connor Doğacan S. Öztürk Amy M. Keesee Victor Pinto Md Shaad Mahmud Chigomezyo Ngwira Shishir Priyadarshi Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach Frontiers in Astronomy and Space Sciences space weather GIC geomagnetic storms ground geomagnetic field machine learning neural networks |
| title | Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach |
| title_full | Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach |
| title_fullStr | Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach |
| title_full_unstemmed | Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach |
| title_short | Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach |
| title_sort | multi variate lstm prediction of alaska magnetometer chain utilizing a coupled model approach |
| topic | space weather GIC geomagnetic storms ground geomagnetic field machine learning neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fspas.2022.846291/full |
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