Machine Learning Techniques for Space Weather
Enrico Camporeale, Centrum Wiskunde & Informatica (CWI);
Simon Wing, Johns Hopkins University Applied Physics Laboratory;
Jay Johnson, Andrews University
Elsevier Science, 2018
ISBN: 978-0-12-811788-0;
Language: English
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. This book can be used as a resource for space physicists, space weather professionals, and computer scientists in related fields.
Features
- Collects many representative nontraditional approaches to space weather into a single volume
- Covers, in an accessible way, the mathematical background not often explained in detail for space scientists
- Chapter 15 on coronal holes detection is accompanied by online MATLAB files, some of which use Statistics and Machine Learning Toolbox, that allow for replication of results in the book, also familiarizing readers with algorithms.
