Machine learning techniques for space weather. (2018)

• Record Type: Book
• Further Information: Note: Edited by Enrico Camporeale, Simon Wing, Jay Johnson.
• Editors: Camporeale, Enrico; Wing, Simon; Johnson, Jay
• Contents: Space Weather 1. Societal and Economic Importance of Space Weather 2. Data Availability and Forecast Products for Space Weather Machine Learning 3. Information Theory 4. Regression 5. Classification Applications 6. Geo-effectiveness of Solar Wind Parameter: An Information Theory Approach 7. Emergence of Dynamical Complexity in the Earth's Magnetosphere 8. Applications of NARMAX in Space Weather 9. Many Hours Ahead Prediction of Geomagnetic Storms with Gaussian Processes 10. Prediction of Mev Electron Fluxes with Autoregressive Models 11. Forecast of Solar Wind Parameters Using Kalman Filter 12. Artificial Neural Networks for Determining Magnetospheric Conditions 13. Reconstruction of Plasma Electron Density from Satellite Measurements via Artifical Neural Networks 14. Classification of Magnetospheric Particle Distributions via NN 15. Automated Solar Flare Prediction 16. Coronal Holes Detection using Supervised Classification 17. CME Classification via k- means Clustering Algorithm
• Publisher Details: Amsterdam : Elsevier
• Publication Date: 2018
• Extent: 1 online resource
• Subjects: 629.4160285631; Space environment; Space environment -- Data processing; Machine learning
• Languages: English
• ISBNs: 9780128117897
• Related ISBNs: 9780128117880
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.