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Full Description
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.
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
