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Book Synopsis

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.

Emerging AI Applications in Earth Sciences

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A Hardback by Chander Prabha

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    View other formats and editions of Emerging AI Applications in Earth Sciences by Chander Prabha

    Publisher: Springer
    Publication Date: 7/14/2025
    ISBN13: 9783031845826, 978-3031845826
    ISBN10: 303184582X
    Also in:
    Computer science

    Description

    Book Synopsis

    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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