Machine learning techniques for space weather

著者

    • Camporeale, Enrico
    • Wing, Simon
    • Johnson, Jay

書誌事項

Machine learning techniques for space weather

edited by Enrico Camporeale, Simon Wing, Jay Johnson

Elsevier, c2018

大学図書館所蔵 件 / 全3件

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields.

目次

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

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