Machine Learning in Earth, Environmental and Planetary Sciences

Theoretical and Practical Applications

Hossein Bonakdari, Isa Ebtehaj, Joseph D. Ladouceur

• Publisher: Elsevier
• Year: 2023
• Print ISBN: 9780443152849
• eBook ISBN: 9780443152856
• Topic: Physical Sciences
• Subtopic: Business Intelligence

Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation. This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results. - Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data - Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes - Includes numerous figures, illustrations and tables to help readers better understand the concepts covered

Table of contents

• Machine Learning in Earth, Environmental and Planetary Sciences
• Cover
• Title Page
• Copyright
• Dedication
• Contents
• About the authors
• Preface
• Acknowledgments
• About the cover image
• Chapter 1 Dataset preparation
• Chapter 2 Preprocessing approaches
• Chapter 3 Postprocessing approaches
• Chapter 4 Non-tuned single-layer feed-forward neural network learning machine—concept
• Chapter 5 Non-tuned single-layer feed-forward neural network learning machine—coding and implementation
• Chapter 6 Outlier-based models of the non-tuned neural network—concept
• Chapter 7 Outlier-based models of the non-tuned neural network—coding and implementation
• Chapter 8 Online sequential non-tuned neural network—concept
• Chapter 9 Online sequential nontuned neural network—coding and implementation
• Chapter 10 Self-adaptive evolutionary of non-tuned neural network—concept
• Chapter 11 Self-adaptive evolutionary of non-tuned neural network—coding and implementation
• Index