Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications

Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing extreme learning machine and neural networks 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 post-processing techniques required for correct data interpretation. Machine Learning in Earth, Environmental and Planetary Sciences provides students, academic and researchers with detailed understanding of how neural networks work, how to prepare data and how to interpret the results. Describes how to apply different schemes of non-tuned rapid machine learning to Earth and Planetary, and Environmental 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 INDICE: Preface Acknowledgments Author Biographies 1. Dataset Preparation 2. Pre-processing approaches 3. Post-processing approaches 4. Non-tuned single-layer feed-forward neural network Learning Machine - Concept 5. Non-tuned single-layer feed-forward neural network Learning Machine - Coding and implementation 6. Outlier-based models of the non-tuned neural network - Concept 7. Outlier-based models of the non-tuned neural network - Coding and implementation 8. Online Sequential non-tuned neural network - Concept 9. Online Sequential non-tuned neural network - Coding and implementation 10. Self-Adaptive Evolutionary of non-tuned neural network - Concept 11. Self-Adaptive Evolutionary of non-tuned neural network - Coding and implementation

• ISBN: 978-0-443-15284-9
• Editorial: Elsevier
• Encuadernacion: Rústica
• Fecha Publicación: 01/07/2023
• Nº Volúmenes: 1
• Idioma: Inglés