Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024)
Abstract Abduallah et al. (2024b, https://doi.org/10.1029/2023sw003824) proposed a novel approach using a deep neural network model, which includes a graph neural network and a bidirectional LSTM layer, named SYMHnet, to forecast the SYM‐H index one and 2 hr in advance. Additionally, the network als...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-08-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW003909 |
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| _version_ | 1841536446064754688 |
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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 Abduallah et al. (2024b, https://doi.org/10.1029/2023sw003824) proposed a novel approach using a deep neural network model, which includes a graph neural network and a bidirectional LSTM layer, named SYMHnet, to forecast the SYM‐H index one and 2 hr in advance. Additionally, the network also provides an uncertainty quantification of the predictions. While the approach is innovative, there are some areas where the model's design and implementation may not align with best practices in uncertainty quantification and predictive modeling. We focus on discrepancies in the input and output of the model, which can limit the applicability in real‐world forecasting scenarios. This comment aims to clarify these issues, offering detailed insights into how such discrepancies could compromise the model's interpretability and reliability, thereby contributing to the advancement of predictive modeling in space weather research. |
| format | Article |
| id | doaj-art-e1bb18208aa84ad8a3aa829cddf6102c |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-e1bb18208aa84ad8a3aa829cddf6102c2025-01-14T16:27:32ZengWileySpace Weather1542-73902024-08-01228n/an/a10.1029/2024SW003909Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024)Armando 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 Abduallah et al. (2024b, https://doi.org/10.1029/2023sw003824) proposed a novel approach using a deep neural network model, which includes a graph neural network and a bidirectional LSTM layer, named SYMHnet, to forecast the SYM‐H index one and 2 hr in advance. Additionally, the network also provides an uncertainty quantification of the predictions. While the approach is innovative, there are some areas where the model's design and implementation may not align with best practices in uncertainty quantification and predictive modeling. We focus on discrepancies in the input and output of the model, which can limit the applicability in real‐world forecasting scenarios. This comment aims to clarify these issues, offering detailed insights into how such discrepancies could compromise the model's interpretability and reliability, thereby contributing to the advancement of predictive modeling in space weather research.https://doi.org/10.1029/2024SW003909machine learninguncertaintygeomagnetic indices forecasting |
| spellingShingle | Armando Collado‐Villaverde Pablo Muñoz Consuelo Cid Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024) Space Weather machine learning uncertainty geomagnetic indices forecasting |
| title | Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024) |
| title_full | Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024) |
| title_fullStr | Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024) |
| title_full_unstemmed | Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024) |
| title_short | Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024) |
| title_sort | comment on prediction of the sym h index using a bayesian deep learning method with uncertainty quantification by abduallah et al 2024 |
| topic | machine learning uncertainty geomagnetic indices forecasting |
| url | https://doi.org/10.1029/2024SW003909 |
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