Data Analytics and Artificial Intelligence for Earth Resource Management offers a detailed look at the different ways data analytics and artificial intelligence can help organizations make better-informed decisions, improve operations, and minimize the negative impacts of resource extraction on the environment. The book explains several different ways data analytics and artificial intelligence can improve and support earth resource management. Predictive modeling can help organizations understand the impacts of different management decisions on earth resources, such as water availability, land use, and biodiversity. Resource monitoring tracks the state of earth resources in real-time, identifying issues and opportunities for improvement. Providing managers with real-time data and analytics allows them to make more informed choices. Optimizing resource management decisions help to identify the most efficient and effective ways to allocate resources. Predictive maintenance allows organizations to anticipate when equipment might fail and take action to prevent it, reducing downtime and maintenance costs. Remote sensing with image processing and analysis can be used to extract information from satellite images and other remote sensing data, providing valuable information on land use, water resources, and other earth resources.
1. Data Analytics and Artificial Intelligence for Earth Resource Management - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page v 7. Contents - p. Page viii 8. List of contributors - p. Page ix 9. Preface - p. Page ix 10. Acknowledgments - p. Page xix 11. List of Illustrations - p. Page xxii 12. List of Tables - p. Page xxiii 13. Chapter 1 : Data analytics and artificial intelligence in Earth resource management - p. Page xxiii 14. Chapter 2 : Data analytics enabled by the Internet of Things and artificial intelligence for the management of Earth’s resources - p. Page 11 15. Chapter 3 : Data preprocessing techniques for earth resource management - p. Page 33 16. Chapter 4 : Artificial intelligence for sustainable stewardship of Earth resources - p. Page 57 17. Chapter 5 : Advancing earth resource management through AI enhanced early warning systems and crisis communication - p. Page 74 18. Chapter 6 : Artificial intelligence for analytical evaluation of landslide vulnerability - p. Page 91 19. Chapter 7 : Socioeconomic and environmental impacts analysis for climate resilient Earth resource management - p. Page 111 20. Chapter 8 : Data analytics for drought vulnerability under climate change scenarios - p. Page 132 21. Chapter 9 : Natural Language Processing for Earth resource management: a case of H2 Golden Retriever research - p. Page 151 22. Chapter 10 : Artificial intelligence in efficient management of water resources - p. Page 181 23. Chapter 11 : Groundwater potential zone evaluations for improving resource management with spatial analysis approach - p. Page 200 24. Chapter 12 : Future trends in computational data analytics and artificial intelligence for Earth resource management - p. Page 235 25. Index - p. Page 262
Data Analytics and Artificial Intelligence for Earth Resource Management offers a detailed look at the different ways data analytics and artificial intelligence can help organizations make better-informed decisions, improve operations, and minimize the negative impacts of resource extraction on the environment. The book explains several different ways data analytics and artificial intelligence can improve and support earth resource management. Predictive modeling can help organizations understand the impacts of different management decisions on earth resources, such as water availability, land use, and biodiversity. Resource monitoring tracks the state of earth resources in real-time, identifying issues and opportunities for improvement. Providing managers with real-time data and analytics allows them to make more informed choices. Optimizing resource management decisions help to identify the most efficient and effective ways to allocate resources. Predictive maintenance allows organizations to anticipate when equipment might fail and take action to prevent it, reducing downtime and maintenance costs. Remote sensing with image processing and analysis can be used to extract information from satellite images and other remote sensing data, providing valuable information on land use, water resources, and other earth resources.
1. Data Analytics and Artificial Intelligence for Earth Resource Management - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page v 7. Contents - p. Page viii 8. List of contributors - p. Page ix 9. Preface - p. Page ix 10. Acknowledgments - p. Page xix 11. List of Illustrations - p. Page xxii 12. List of Tables - p. Page xxiii 13. Chapter 1 : Data analytics and artificial intelligence in Earth resource management - p. Page xxiii 14. Chapter 2 : Data analytics enabled by the Internet of Things and artificial intelligence for the management of Earth’s resources - p. Page 11 15. Chapter 3 : Data preprocessing techniques for earth resource management - p. Page 33 16. Chapter 4 : Artificial intelligence for sustainable stewardship of Earth resources - p. Page 57 17. Chapter 5 : Advancing earth resource management through AI enhanced early warning systems and crisis communication - p. Page 74 18. Chapter 6 : Artificial intelligence for analytical evaluation of landslide vulnerability - p. Page 91 19. Chapter 7 : Socioeconomic and environmental impacts analysis for climate resilient Earth resource management - p. Page 111 20. Chapter 8 : Data analytics for drought vulnerability under climate change scenarios - p. Page 132 21. Chapter 9 : Natural Language Processing for Earth resource management: a case of H2 Golden Retriever research - p. Page 151 22. Chapter 10 : Artificial intelligence in efficient management of water resources - p. Page 181 23. Chapter 11 : Groundwater potential zone evaluations for improving resource management with spatial analysis approach - p. Page 200 24. Chapter 12 : Future trends in computational data analytics and artificial intelligence for Earth resource management - p. Page 235 25. Index - p. Page 262
