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Emerging AI Applications in Earth Sciences Challenges, Impact and Analysis /

This proposed book provides deeper insights into artificial intelligence techniques and procedures available for earth sciences. This book unveils several applications of metaheuristic approaches (i.e., swarm intelligence and IoT technologies) in collaboration with AI for earth sciences. It presents...

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Bibliografiske detaljer
Institution som forfatter: SpringerLink (Online service)
Andre forfattere: Prabha, Chander (Editor), Hassan, Md. Mehedi (Editor), Yasmin, Farhana (Editor), Karim, Asif (Editor)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Udgivelse:1st ed. 2025.
Serier:Sustainable Artificial Intelligence-Powered Applications, IEREK Interdisciplinary Series for Sustainable Development,
Fag:
Computer science.
Artificial intelligence.
Machine learning.
Earth sciences.
Computer Science.
Artificial Intelligence.
Machine Learning.
Earth Sciences.
Online adgang:https://doi.org/10.1007/978-3-031-84583-3
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Indholdsfortegnelse:
  • Chapter 1 Introduction to AI and IoT in the field of Earth Sciences
  • Chapter 2. Growing Beyond the Earth: The Potential of Extra-Terrestrial Agriculture from Earth to Space
  • Chapter 3. A Spatiotemporal Urban Growth Assessment in Bhopal, India from 1992 To 2042 using Machine Learning Algorithms
  • Chapter 4. Emerging Areas and Applications in the Field of Earth Sciences
  • Chapter 5. Integrating Deep Learning and IoT for Enhanced Monitoring and Sustainable Mining Practices
  • Chapter 6. AI-Driven Insights into Fault Movements and Earthquake Dynamics
  • Chapter 7. Harnessing Ai for Seismic Hazard Detection and Prediction: Innovations and Challenges
  • Chapter 8. AI Techniques for Remote Monitoring
  • Chapter 9. Role of AI in Estimating Potential Aftershocks During Earthquake
  • Chapter 10. Advancements in Ozone Monitoring: Leveraging AI and ML for Environmental Protection
  • Chapter 11. Quantum Computing in the Field of Earth Sciences
  • Chapter 12. Machine Learning Approaches for Yield Prediction and Crop Management Optimization: A SLR
  • Chapter 13. Resource Allocation in agriculture and Water Management fields
  • Chapter 14. Optimising Crop Yields with Machine learning: Techniques and Applications
  • Chapter 15. AI Trends Concerning Patterns, Anomalies, and Correlations for Predicting Earthquake Patterns
  • Chapter 16. Future Trends and Challenges of AI And IoT for Earth Sciences.

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