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Machine learning techniques for space weather /
"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 d...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
Elsevier,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Machine Learning Techniques for Space Weather; Copyright; Contents; Contributors; Introduction; Machine Learning and Space Weather; Scope and Structure of the Book; Acknowledgments; References; Part I: Space Weather; Chapter 1: Societal and Economic Importance of Space Weather; 1 What is Space Weather?; 2 Why Now?; 3 Impacts; 3.1 Geomagnetically Induced Currents; 3.2 Global Navigation Satellite Systems; 3.3 Single-Event Effects; 3.4 Other Radio Systems; 3.5 Satellite Drag; 4 Looking to the Future; 5 Summary and Conclusions; Acknowledgments; References.
- Chapter 2: Data Availability and Forecast Products for Space Weather1 Introduction; 2 Data and Models Based on Machine Learning Approaches; 3 Space Weather Agencies; 3.1 Government Agencies; 3.1.1 NOAA's Data and Products; 3.1.2 NASA; 3.1.3 European Space Agency; 3.1.4 The US Air Force Weather Wing; 3.2 Academic Institutions; 3.2.1 Kyoto University, Japan; 3.2.2 Rice University, USA; 3.2.3 Laboratory for Atmospheric and Space Physics, USA; 3.3 Commercial Providers; 3.4 Other Nonprofit, Corporate Research Agencies; 3.4.1 USGS; 3.4.2 JHU Applied Physics Lab.
- 3.4.3 US Naval Research Lab3.4.4 Other International Service Providers; 4 Summary; References; Part II: Machine Learning; Chapter 3: An Information-Theoretical Approach to Space Weather; 1 Introduction; 2 Complex Systems Framework; 3 State Variables; 4 Dependency, Correlations, and Information; 4.1 Mutual Information as a Measure of Nonlinear Dependence; 4.2 Cumulant-Based Cost as a Measure of Nonlinear Dependence; 4.3 Causal Dependence; 4.4 Transfer Entropy and Redundancy as Measures of Causal Relations; 4.5 Conditional Redundancy; 4.6 Significance of Discriminating Statistics.
- 4.7 Mutual Information and Information Flow5 Examples From Magnetospheric Dynamics; 6 Significance as an Indicator of Changes in Underlying Dynamics; 6.1 Detecting Dynamics in a Noisy System; 6.2 Cumulant-Based Information Flow; 7 Discussion; 8 Summary; Acknowledgments; References; Chapter 4: Regression; 1 What is Regression?; 2 Learning From Noisy Data; 2.1 Prediction Errors; 2.2 A Probabilistic Set-Up; 2.3 The Least Squares Method for Linear Regression; 2.3.1 The Least Squares Method and the Best Linear Predictor.
- 2.3.2 The Least Squares Method and the Maximum Likelihood Principle2.3.3 A More General Approach and Higher-Order Predictors; 2.4 Overfitting; 2.4.1 The Order Selection Problem; Error Decomposition: The Bias Versus Variance Trade-Off; Some Popular Order Selection Criteria; 2.4.2 Regularization; 2.5 From Point Predictors to Interval Predictors; 2.5.1 Distribution-Free Interval Predictors; 2.6 Probability Density Estimation; 3 Predictions Without Probabilities; 3.1 Approximation Theory; Dense Sets; Best Approximator; 3.1.1 Neural Networks.