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Call number: M 18.91612

Description / Table of Contents: 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 Weather -- 1 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 Lab -- 3.4.4 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

Description / Table of Contents: 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 -- 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 Principle -- 2.3.3 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 -- 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 -- 5.2 Nonlinear Systems -- References -- Chapter 5: Supervised Classification: Quite a Brief Overview -- 1 Introduction -- 1.1 Learning, Not Modeling -- 1.2 An Outline -- 2 Classifiers -- 2.1 Preliminaries -- 2.2 The Bayes Classifier -- 2.3 Generative Probabilistic Classifiers -- 2.4 Discriminative Probabilistic Classifiers -- 2.5 Losses and Hypothesis Spaces -- 2.5.1 0-1 Loss -- 2.5.2 Convex Surrogate Losses

Description / Table of Contents: 2.5.3 Particular Surrogate Losses -- 2.6 Neural Networks -- 2.7 Neighbors, Trees, Ensembles, and All that -- 2.7.1 k Nearest Neighbors -- 2.7.2 Decision Trees -- 2.7.3 Multiple Classifier Systems -- 3 Representations and Classifier Complexity -- 3.1 Feature Transformations -- 3.1.1 The Kernel Trick -- 3.2 Dissimilarity Representation -- 3.3 Feature Curves and the Curse of Dimensionality -- 3.4 Feature Extraction and Selection -- 4 Evaluation -- 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 -- 5 Regularization -- 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 -- 6.6 Active Learning -- Acknowledgments -- References -- Part III: Applications -- Chapter 6: Untangling the Solar Wind Drivers of the Radiation Belt: An Information Theoretical Approach -- 1 Introduction -- 2 Data Set -- 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 Discussion -- 5.1 Geo-Effectiveness of Solar Wind Velocity -- 5.2 nsw and Vsw Anticorrelation

Description / Table of Contents: 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 -- 5.5.3 Prediction Horizon -- 6 Summary -- Acknowledgments -- References -- Chapter 7: Emergence of Dynamical Complexity in the Earth's Magnetosphere -- 1 Introduction -- 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 -- 7 Summary -- Acknowledgments -- References -- Chapter 8: Applications of NARMAX in Space Weather -- 1 Introduction -- 2 NARMAX Methodology -- 2.1 Forward Regression Orthogonal Least Square -- 2.2 The Noise Model -- 2.3 Model Validation -- 2.4 Summary -- 3 NARMAX and Space Weather Forecasting -- 3.1 Geomagnetic Indices -- 3.1.1 SISO Dst Index -- 3.1.2 Continuous Time Dst model -- 3.1.3 MISO Dst -- 3.1.4 Kp Index -- 3.2 Radiation Belt Electron Fluxes -- 3.2.1 GOES High Energy -- 3.2.2 SNB3GEO Comparison With NOAA REFM -- 3.2.3 GOES Low Energy -- 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 -- References -- Chapter 9: Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models -- 1 Geomagnetic Time Series and Forecasting -- 2 Dst Forecasting

Description / Table of Contents: 2.1 Models and Algorithms -- 2.2 Probabilistic Forecasting -- 3 Gaussian Processes -- 3.1 Gaussian Process Regression: Formulation -- 3.2 Gaussian Process Regression: Inference -- 4 One-Hour Ahead Dst Prediction -- 4.1 Data Source: OMNI -- 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 -- 5.2 Choice of Kernel -- 5.3 Model Selection: Hyperparameters -- 5.3.1 Grid Search -- 5.3.2 Coupled Simulated Annealing -- 5.3.3 Maximum Likelihood -- 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 -- 8.3 Final Evaluation -- 8.4 Sample Predictions With Error Bars -- 9 Conclusion -- References -- 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 -- 4 Summary -- References -- Chapter 11: Artificial Neural Networks for Determining Magnetospheric Conditions -- 1 Introduction -- 2 A Brief Review of ANNs -- 3 Methodology and Application -- 3.1 The DEN2D Model -- 4 Advanced Applications -- 4.1 The DEN3D Model -- 4.2 The Chorus and Hiss Wave Models -- 4.3 Radiation Belt Flux Modeling -- 5 Summary and Discussion -- Acknowledgments -- References -- Chapter 12: Reconstruction of Plasma Electron Density From Satellite Measurements Via Artificial Neural Networks

Description / Table of Contents: 1 Overview

Type of Medium: Monograph available for loan

Pages: xviii, 433 Seiten , Illustrationen

ISBN: 978-0-12-811788-0

Classification:

Geophysics

Language: English

Location: Upper compact magazine

Branch Library: GFZ Library