Camporeale E., Wing S., Johnson J. Machine Learning Techniques for Space Weather

Elsevier, 2018. — 436 p. — ISBN: 978-0-12-811788-0.This book 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.Key Features
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 algorithmsReadership
Space physicists, space weather professionals, computer scientists in related fields, information and data scientists in related fieldsSpace Weather
Societal and Economic Importance of Space Weather
Data Availability and Forecast Products for Space Weather
Machine Learning
Information Theory
Regression
Classification
Applications
Geo-effectiveness of Solar Wind Parameter: An Information Theory Approach
Emergence of Dynamical Complexity in the Earth's Magnetosphere
Applications of NARMAX in Space Weather
Many Hours Ahead Prediction of Geomagnetic Storms with Gaussian Processes
Prediction of Mev Electron Fluxes with Autoregressive Models
Forecast of Solar Wind Parameters Using Kalman Filter
Artificial Neural Networks for Determining Magnetospheric Conditions
Reconstruction of Plasma Electron Density from Satellite Measurements via Artifical Neural Networks
Classification of Magnetospheric Particle Distributions via NN
Automated Solar Flare Prediction
Coronal Holes Detection using Supervised Classification
CME Classification via k-means Clustering AlgorithmTrue PDF