Chapter
3.1 Geomagnetically Induced Currents
3.2 Global Navigation Satellite Systems
5 Summary and Conclusions
Chapter 2: Data Availability and Forecast Products for Space Weather
2 Data and Models Based on Machine Learning Approaches
3.1.1 NOAA's Data and Products
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
3.4 Other Nonprofit, Corporate Research Agencies
3.4.2 JHU Applied Physics Lab
3.4.3 US Naval Research Lab
3.4.4 Other International Service Providers
Part II: Machine Learning
Chapter 3: An Information-Theoretical Approach to Space Weather
2 Complex Systems Framework
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
4.4 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 Flow
5 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
2 Learning From Noisy Data
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 Principle
2.3.3 A More General Approach and Higher-Order Predictors
2.4.1 The Order Selection Problem
Error Decomposition: The Bias Versus Variance Trade-Off
Some Popular Order Selection Criteria
2.5 From Point Predictors to Interval Predictors
2.5.1 Distribution-Free Interval Predictors
2.6 Probability Density Estimation
3 Predictions Without Probabilities
The Backpropagation Algorithm: High-Level Idea
Multiple Layers Networks (Deep Networks)
4 Probabilities Everywhere: Bayesian Regression
4.1 Gaussian Process Regression
5 Learning in the Presence of Time: Identification of Dynamical Systems
5.1 Linear Time-Invariant Systems
Chapter 5: Supervised Classification: Quite a Brief Overview
1.1 Learning, Not Modeling
2.3 Generative Probabilistic Classifiers
2.4 Discriminative Probabilistic Classifiers
2.5 Losses and Hypothesis Spaces
2.5.2 Convex Surrogate Losses
2.5.3 Particular Surrogate Losses
2.7 Neighbors, Trees, Ensembles, and All that
2.7.1 k Nearest Neighbors
2.7.3 Multiple Classifier Systems
3 Representations and Classifier Complexity
3.1 Feature Transformations
3.2 Dissimilarity Representation
3.3 Feature Curves and the Curse of Dimensionality
3.4 Feature Extraction and Selection
4.1 Apparent Error and Holdout Set
4.2 Resampling Techniques
4.2.1 Leave-One-Out and k-Fold Cross-Validation
4.2.2 Bootstrap Estimators
4.2.3 Tests of Significance
4.3 Learning Curves and the Single Best Classifier
4.4 Some Words About More Realistic Scenarios
6 Variations on Standard Classification
6.1 Multiple Instance Learning
6.2 One-Class Classification, Outliers, and Reject Options
6.3 Contextual Classification
6.4 Missing Data and Semisupervised Learning
6.5 Transfer Learning and Domain Adaptation
Chapter 6: Untangling the Solar Wind Drivers of the Radiation Belt: An Information Theoretical Approach
3 Mutual Information, Conditional Mutual Information, and Transfer Entropy
4 Applying Information Theory to Radiation Belt MeV Electron Data
4.1 Radiation Belt MeV Electron Flux Versus Vsw
4.2 Radiation Belt MeV Electron Flux Versus nsw
4.3 Anticorrelation of Vsw and nsw and Its Effect on Radiation Belt
4.4 Ranking of Solar Wind Parameters Based on Information Transfer to Radiation Belt Electrons
4.5 Detecting Changes in the System Dynamics
5.1 Geo-Effectiveness of Solar Wind Velocity
5.2 nsw and Vsw Anticorrelation
5.3 Geo-Effectiveness of Solar Wind Density
5.4 Revisiting the Triangle Distribution
5.5 Improving Models With Information Theory
5.5.1 Selecting Input Parameters
5.5.2 Detecting Nonstationarity in System Dynamics
Chapter 7: Emergence of Dynamical Complexity in the Earth's Magnetosphere
2 On Complexity and Dynamical Complexity
3 Coherence and Intermittent Features in Time Series Geomagnetic Indices
4 Scale-Invariance and Self-Similarity in Geomagnetic Indices
5 Near-Criticality Dynamics
6 Multifractional Features and Dynamical Phase Transitions
Chapter 8: Applications of NARMAX in Space Weather
2.1 Forward Regression Orthogonal Least Square
3 NARMAX and Space Weather Forecasting
3.1.2 Continuous Time Dst model
3.2 Radiation Belt Electron Fluxes
3.2.2 SNB3GEO Comparison With NOAA REFM
3.3 Summary of NARMAX Models
4 NARMAX and Insight Into the Physics
4.1 NARMAX Deduced Solar Wind-Magnetosphere Coupling Function
4.2 Identification of Radiation Belt Control Parameters
4.2.1 Solar Wind Density Relationship With Relativistic Electrons at GEO
4.2.2 Geostationary Local Quasilinear Diffusion vs. Radial Diffusion
4.3 Frequency Domain Analysis of the Dst Index
5 Discussions and Conclusion
Chapter 9: Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models
1 Geomagnetic Time Series and Forecasting
2.1 Models and Algorithms
2.2 Probabilistic Forecasting
3.1 Gaussian Process Regression: Formulation
3.2 Gaussian Process Regression: Inference
4 One-Hour Ahead Dst Prediction
4.2 Gaussian Process Dst Model
4.3 Gaussian Process Auto-Regressive (GP-AR)
4.4 GP-AR With eXogenous Inputs (GP-ARX)
5 One-Hour Ahead Dst Prediction: Model Design
5.1 Choice of Mean Function
5.3 Model Selection: Hyperparameters
5.3.2 Coupled Simulated Annealing
5.4 Model Selection: Auto-Regressive Order
6 GP-AR and GP-ARX: Workflow Summary
7 Practical Issues: Software
8 Experiments and Results
8.1 Model Selection and Validation Performance
8.2 Comparison of Hyperparameter Selection Algorithms
8.4 Sample Predictions With Error Bars
Chapter 10: Prediction of MeV Electron Fluxes and Forecast Verification
1 Relativistic Electrons in Earth's Outer Radiation Belt
1.1 Source, Loss, Transport, and Acceleration, Variation
2 Numerical Techniques in Radiation Belt Forecasting
3 Relativistic Electron Forecasting and Verification
3.1 Forecast Verification
3.2 Relativistic Electron Forecasting
Chapter 11: Artificial Neural Networks for Determining Magnetospheric Conditions
3 Methodology and Application
4.2 The Chorus and Hiss Wave Models
4.3 Radiation Belt Flux Modeling
Chapter 12: Reconstruction of Plasma Electron Density From Satellite Measurements Via Artificial Neural Networks
1.1 Space Weather-Related Aspects and Motivation
1.1.1 Plasma Density and the Plasmasphere
1.1.2 Determining the Electron Density From Upper-Hybrid Band Resonance Frequency
1.2 Brief Background on Neural Networks
1.2.1 Basic Concepts Related to Neural Networks
1.2.2 Neural Network Design Flow
1.2.3 Importance of Validation
2 Implementation of the Algorithm
2.2 Neural Network Architecture
2.3 Steps of the Design Flow
3.1 Comparison With AURA and NURD Performance
3.2 Comparison With Empirical Model of sheeley2001empirical
4 Discussion and Future Directions
Chapter 13: Classification of Magnetospheric Particle Distributions Via Neural Networks
2 A Brief Introduction to the Earth's Magnetosphere
3 Pitch Angle Distributions in the Magnetosphere
4 Neural Networks Applied to Magnetospheric Particle Distribution Classification
4.1 Basic Concepts and Applications of Neural Networks
4.2.1 Mathematical Background for the SOM's Learning Algorithm Implementation
4.2.2 Geometrical Interpretation of the SOM's Learning Algorithm
4.3 PAD Classification of Relativistic and Subrelativistic Electrons in the Van Allen Radiation Belts
4.3.1 Step 1: Data Choice for the SOM's Training Phase
4.3.2 Step 2: Preparing the Data to Use It as Input to the SOM
4.3.3 Step 3: Determining the Classes Outputted by the SOM
4.3.4 Step 4: Displaying Clustered Particle PAD Shapes as a Function of Radial Distance and Time
Chapter 14: Machine Learning for Flare Forecasting
1 The Solar Flare Prediction Problem
2 Standard Machine Learning Methods
3 Advanced Machine Learning Methods
4 Innovative Machine Learning Methods
5 The Technological Aspect
Chapter 15: Coronal Holes Detection Using Supervised Classification
2.1 Coronal Hole Feature Extraction
2.1.2 Modified SPoCA-CH Module
2.3.3 Magnetic Flux Imbalance
2.3.4 First- and Second-Order Statistics
2.3.5 Sets of Attributes Used for Classification
3 Supervised Classification
3.1 Supervised Classification Algorithms
3.2.1 Cost-Sensitive Learning
3.3 Training and Evaluation Protocol
3.3.2 Hyperparameter Optimization During Training
4.1 Cost-Sensitive Learning Versus Sampling Techniques
4.3 Importance of Attributes
5 Discussion and Conclusion
Appendix A First-Order Image Statistics
Appendix C Classifier Hyperparameter Range
Appendix D Relevance of Attributes
Chapter 16: Solar Wind Classification Via k-Means Clustering Algorithm
2 Basic Assumptions and Methodology
4 Comparing 2-Means Clustering to Existing Solar Wind Categorization Schemes
5 Model Selection, or How to Choose k
6 Interpreting Clustering Results
7 Using k-Means for Feature Selection
Machine Learning Techniques for Space Weather
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