Machine Learning Techniques for Space Weather 1st Edition Enrico Camporeale

Machine Learning Techniques for Space Weather 1st Edition Enrico Camporeale – Ebook Instant Download/Delivery ISBN(s): 9780128117880,0128117885, 9780128117897, 0128117893

Product details:

• ISBN 10: 0128117893
• ISBN 13: 9780128117897
• Author:  Enrico Camporeale

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.

• Collects many representative non-traditional approaches to space weather into a single volume
• Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists
• Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms

Table contents:

1. Chapter 1: Societal and Economic Importance of Space Weather
2. Chapter 2: Data Availability and Forecast Products for Space Weather
3. Chapter 3: An Information-Theoretical Approach to Space Weather
4. Chapter 4: Regression
5. Chapter 5: Supervised Classification: Quite a Brief Overview
6. Chapter 6: Untangling the Solar Wind Drivers of the Radiation Belt: An Information Theoretical Appr
7. Chapter 7: Emergence of Dynamical Complexity in the Earth’s Magnetosphere
8. Chapter 8: Applications of NARMAX in Space Weather
9. Chapter 9: Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models
10. Chapter 10: Prediction of MeV Electron Fluxes and Forecast Verification
11. Chapter 11: Artificial Neural Networks for Determining Magnetospheric Conditions
12. Chapter 12: Reconstruction of Plasma Electron Density From Satellite Measurements Via Artificial Ne
13. Chapter 13: Classification of Magnetospheric Particle Distributions Via Neural Networks
14. Chapter 14: Machine Learning for Flare Forecasting
15. Chapter 15: Coronal Holes Detection Using Supervised Classification
16. Chapter 16: Solar Wind Classification Via k-Means Clustering Algorithm

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