| _version_ | 1866901151075532800 |
|---|---|
| author | Mohammed, Muzammil Mohammed, Haroon Ayan Ali Mohammed, Ehsanullah Arjumand Mohammed, Azharuddin T, Anusha |
| author_facet | Mohammed, Muzammil Mohammed, Haroon Ayan Ali Mohammed, Ehsanullah Arjumand Mohammed, Azharuddin T, Anusha |
| contents | <p class="p1"><span>Coronal Mass Ejection (CME) events pose an escalating threat to the stability of global infrastructure, particularly power grids, telecommunication networks, aviation systems, and orbiting satellites. When these colossal eruptions of magnetized plasma interact with Earth’s magnetic field, they can trigger geomagnetic storms capable of inflicting multi-trillion-dollar economic losses and widespread technological disruption. Despite advances in Helio physics and satellite observation, existing CME forecasting systems continue to offer extremely limited warning windows, often less than an hour, leaving governments, industries, and mission-critical systems with little time to respond effectively.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19718417 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Project Phoenix – AI Driven CME Detection Mohammed, Muzammil Mohammed, Haroon Ayan Ali Mohammed, Ehsanullah Arjumand Mohammed, Azharuddin T, Anusha CME Detection Aditya-L1 LSTM Autoencoder Gradient Boosting SHAP Solar Wind Space Weather <p class="p1"><span>Coronal Mass Ejection (CME) events pose an escalating threat to the stability of global infrastructure, particularly power grids, telecommunication networks, aviation systems, and orbiting satellites. When these colossal eruptions of magnetized plasma interact with Earth’s magnetic field, they can trigger geomagnetic storms capable of inflicting multi-trillion-dollar economic losses and widespread technological disruption. Despite advances in Helio physics and satellite observation, existing CME forecasting systems continue to offer extremely limited warning windows, often less than an hour, leaving governments, industries, and mission-critical systems with little time to respond effectively.</span></p> |
| title | Project Phoenix – AI Driven CME Detection |
| topic | CME Detection Aditya-L1 LSTM Autoencoder Gradient Boosting SHAP Solar Wind Space Weather |
| url | https://doi.org/10.5281/zenodo.19718417 |