Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities
With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GA...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/124 |
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| author | Renzhong Zhang Haorui Li Yunxiao Shen Jiayi Yang Wang Li Dongsheng Zhao Andong Hu |
| author_facet | Renzhong Zhang Haorui Li Yunxiao Shen Jiayi Yang Wang Li Dongsheng Zhao Andong Hu |
| author_sort | Renzhong Zhang |
| collection | DOAJ |
| description | With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. In recent years, the application of deep learning technology in ionospheric modeling has achieved breakthrough advancements, significantly impacting navigation, communication, and space weather forecasting. Nevertheless, due to limitations in observational networks and the dynamic complexity of the ionosphere, deep learning-based ionospheric models still face challenges in terms of accuracy, resolution, and interpretability. This paper systematically reviews the development of deep learning applications in ionospheric modeling, summarizing findings that demonstrate how integrating multi-source data and employing multi-model ensemble strategies has substantially improved the stability of spatiotemporal predictions, especially in handling complex space weather events. Additionally, this study explores the potential of deep learning in ionospheric modeling for the early warning of geological hazards such as earthquakes, volcanic eruptions, and tsunamis, offering new insights for constructing ionospheric-geological activity warning models. Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. Overall, deep learning provides a powerful tool for ionospheric modeling and indicates promising prospects for its application in early warning systems and future research. |
| format | Article |
| id | doaj-art-c262e055c1a44e5190f8cbf1e01b2db6 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c262e055c1a44e5190f8cbf1e01b2db62025-01-10T13:20:19ZengMDPI AGRemote Sensing2072-42922025-01-0117112410.3390/rs17010124Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and OpportunitiesRenzhong Zhang0Haorui Li1Yunxiao Shen2Jiayi Yang3Wang Li4Dongsheng Zhao5Andong Hu6Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaCooperative Institute for Research in Environmental Sciences (CIRES), CU Boulder, Boulder, CO 80309, USAWith the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. In recent years, the application of deep learning technology in ionospheric modeling has achieved breakthrough advancements, significantly impacting navigation, communication, and space weather forecasting. Nevertheless, due to limitations in observational networks and the dynamic complexity of the ionosphere, deep learning-based ionospheric models still face challenges in terms of accuracy, resolution, and interpretability. This paper systematically reviews the development of deep learning applications in ionospheric modeling, summarizing findings that demonstrate how integrating multi-source data and employing multi-model ensemble strategies has substantially improved the stability of spatiotemporal predictions, especially in handling complex space weather events. Additionally, this study explores the potential of deep learning in ionospheric modeling for the early warning of geological hazards such as earthquakes, volcanic eruptions, and tsunamis, offering new insights for constructing ionospheric-geological activity warning models. Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. Overall, deep learning provides a powerful tool for ionospheric modeling and indicates promising prospects for its application in early warning systems and future research.https://www.mdpi.com/2072-4292/17/1/124ionospheric modeldeep learningspace weather monitoringnatural disaster early warningnavigation and positioning |
| spellingShingle | Renzhong Zhang Haorui Li Yunxiao Shen Jiayi Yang Wang Li Dongsheng Zhao Andong Hu Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities Remote Sensing ionospheric model deep learning space weather monitoring natural disaster early warning navigation and positioning |
| title | Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities |
| title_full | Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities |
| title_fullStr | Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities |
| title_full_unstemmed | Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities |
| title_short | Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities |
| title_sort | deep learning applications in ionospheric modeling progress challenges and opportunities |
| topic | ionospheric model deep learning space weather monitoring natural disaster early warning navigation and positioning |
| url | https://www.mdpi.com/2072-4292/17/1/124 |
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