Machine Learning Techniques for Space Weather book published

Recently, the book 'Machine Learning Techniques for Space Weather', edited by CWI researcher Enrico Camporeale and others, was published by Elsevier. The book provides “a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals”.

Publication date: 12-07-2018

Recently, the book Machine Learning Techniques for Space Weather, edited by CWI researcher Enrico Camporeale and others, was published by Elsevier. The book provides “a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals”.

Enrico Camporeale explains: “Space weather is the study of the effect of the Sun’s variability on Earth, the complex electromagnetic system surrounding it, and eventually on human life. I believe that it is the right time for a book on machine learning applied to this topic. Many space researchers have realized in recent years that the field can benefit from the impressive breakthroughs of artificial intelligence, as there is a an enormous availability of data from observations.”

“Historically, these data has been analyzed and studied through standard statistical techniques, and often by hand. I am convinced that the algorithms of machine learning will become more and more mainstream in this field. However, many space physicists might still find a barrier in their way when trying to apply machine learning. Indeed, navigating in the sea of toolboxes, algorithms and computer languages can be daunting, time-consuming, and frustrating. In this context, this book offers a gentle introduction aimed at bridging the space physics and machine learning communities.”

Enrico Camporeale is a tenure track researcher in the Multiscale Dynamics research group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. The other editors of the book are Simon Wing from Johns Hopkins University Applied Physics Laboratory and University of Maryland University College, and Jay R. Johnson from Andrews University. The work was partially supported by an NWO-VIDI grant and two NASA grants.

 

More information