Artificial Intelligence in Earth Science
eBook - ePub

Artificial Intelligence in Earth Science

Best Practices and Fundamental Challenges

  1. 430 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Artificial Intelligence in Earth Science

Best Practices and Fundamental Challenges

About this book

Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth Science. The book focuses on the most challenging problems in applying AI in Earth system sciences, such as training data preparation, model selection, hyperparameter tuning, model structure optimization, spatiotemporal generalization, transforming model results into products, and explaining trained models. In addition, it provides full-stack workflow tutorials to help walk readers through the whole process, regardless of previous AI experience. The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work. - Provides practical, step-by-step guides for Earth Scientists who are interested in implementing AI techniques in their work - Features case studies to show real-world examples of techniques described in the book - Includes additional elements to help readers who are new to AI, including end-of-chapter, key concept bulleted lists that concisely cover key concepts in the chapter

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Yes, you can access Artificial Intelligence in Earth Science by Ziheng Sun,Nicoleta Cristea,Pablo Rivas in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Geology & Earth Sciences. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Elsevier
Year
2023
Print ISBN
9780323917377
eBook ISBN
9780323972161
Topic
Physical Sciences
Subtopic
Geology & Earth Sciences

Table of contents

  1. Artificial Intelligence in Earth Science
  2. Cover
  3. Title Page
  4. Copyright
  5. Table of Contents
  6. Contributors
  7. Chapter 1 Introduction of artificial intelligence in Earth sciences
  8. Chapter 2 Machine learning for snow cover mapping
  9. Chapter 3 AI for sea ice forecasting
  10. Chapter 4 Deep learning for ocean mesoscale eddy detection
  11. Chapter 5 Artificial intelligence for plant disease recognition
  12. Chapter 6 Spatiotemporal attention ConvLSTM networks for predicting and physically interpreting wildfire spread
  13. Chapter 7 AI for physics-inspired hydrology modeling
  14. Chapter 8 Theory of spatiotemporal deep analogs and their application to solar forecasting
  15. Chapter 9 AI for improving ozone forecasting
  16. Chapter 10 AI for monitoring power plant emissions from space
  17. Chapter 11 AI for shrubland identification and mapping
  18. Chapter 12 Explainable AI for understanding ML-derived vegetation products
  19. Chapter 13 Satellite image classification using quantum machine learning
  20. Chapter 14 Provenance in earth AI
  21. Chapter 15 AI ethics for earth sciences
  22. Index
  23. Index - Continued