Artificial intelligence in earth science : best practices and fundamental challenges /

Bibliographic Details
Call Number:Libro Electrónico
Other Authors: Sun, Ziheng (Editor), Cristea, Nicoleta (Editor), Rivas Perea, Pablo, 1980- (Editor)
Format: Electronic eBook
Language:Inglés
Published: Amsterdam : Elsevier, 2023.
Subjects:
Artificial intelligence > Geophysical applications.
Earth sciences > Data processing.
Intelligence artificielle > Applications g�eophysiques.
Artificial intelligence > Geophysical applications
Earth sciences > Data processing
Online Access:Texto completo
Table of Contents:
  • <B>Part I: Fundamentals of Earth AI<br></b>1. Basic Concepts of Earth AI<br>2. Introductory AI Algorithms<br>3. AI Infrastructure
  • hardware and software for developing Earth AI<br><br><b>Part II. Existing Best Practices</b><br>4. AI for Earthquake Hidden Signal Detection<br>5. AI for Dust Storm Detection<br>6. AI for Snow Monitoring<br>7. AI for Volcano Pre-warning and Prediction<br>8. AI for Landslide Damage Assessment<br>9. AI for Hurricane Prediction<br>10. AI for Precipitation Prediction<br>11. AI for Drought Monitoring<br>12. AI for Wildfire Detection<br>13. AI for Air Quality Prediction<br>14. AI for Agricultural Irrigation Decision Making<br>15. AI for Land Cover Land Use Classification<br>16. AI for Ocean mesoscale eddies detection<br><br><b>Part III Fundamental Challenges for AI in Earth Sciences</b><br>17. AI Model Selection and Tuning<br>18. Training Data Preparation<br>19. Explainable AI<br>20. AI Generalization <br>21. AI Integration with Physics-based Models <br>22. AI Provenance (Replicability & Reproducibility)<br>23. AI Ethics

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