Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges

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 INDICE: Part I: Fundamentals of Earth AI1. Basic Concepts of Earth AI2. Introductory AI Algorithms3. AI Infrastructure - hardware and software for developing Earth AIPart II. Existing Best Practices4. AI for Earthquake Hidden Signal Detection5. AI for Dust Storm Detection6. AI for Snow Monitoring7. AI for Volcano Pre-warning and Prediction8. AI for Landslide Damage Assessment9. AI for Hurricane Prediction10. AI for Precipitation Prediction11. AI for Drought Monitoring12. AI for Wildfire Detection13. AI for Air Quality Prediction14. AI for Agricultural Irrigation Decision Making15. AI for Land Cover Land Use Classification16. AI for Ocean mesoscale eddies detectionPart III Fundamental Challenges for AI in Earth Sciences17. AI Model Selection and Tuning18. Training Data Preparation19. Explainable AI20. AI Generalization 21. AI Integration with Physics-based Models 22. AI Provenance (Replicability & Reproducibility)23. AI Ethics

• ISBN: 978-0-323-91737-7
• Editorial: Elsevier
• Encuadernacion: Rústica
• Fecha Publicación: 01/03/2023
• Nº Volúmenes: 1
• Idioma: Inglés