Climate change, biodiversity loss, resource depletion, and pollution are intensifying. Addressing these global issues requires fresh perspectives and cutting-edge solutions that integrate AI with environmental sustainability. AI and computational intelligence technologies are evolving at an unprecedented pace, offering new tools and methodologies for tackling environmental challenges. Yet existing resources often fail to capture the latest breakthroughs and interdisciplinary approaches needed to harness these innovations effectively.
AI and Computational Intelligence for Environmental Sustainability showcases the potential of artificial intelligence and computational intelligence in tackling critical environmental challenges. As the urgency to combat climate change, optimize resource utilization, and promote sustainable practices grows, this book provides a comprehensive resource that bridges the gap between AI research and its practical applications in environmental sustainability, and offers insights into the latest developments and methodologies. It highlights real-world applications and case studies to inspire researchers, developers, and policymakers to create and implement AI-driven solutions for a more sustainable future, foster collaboration by bringing together experts from diverse fields to share knowledge and foster interdisciplinary collaboration in the pursuit of environmental sustainability. It also addresses the ethical and practical challenges of applying AI in sustainability, advocating for responsible AI development and low-energy computing practices.
1. Chapter 1. Introduction to artificial intelligence for environmental sustainability - p. 1 2. Chapter 2. Artificial intelligence for climate modeling and prediction - p. 23 3. Chapter 3. Renewable energy optimization with artificial intelligence - p. 39 4. Chapter 4. Smart waste management and recycling using artificial intelligence - p. 63 5. Chapter 5. Artificial intelligence in water resource management - p. 73 6. Chapter 6. Integrated artificial intelligence-enabled precision agriculture system architecture - p. 87 7. Chapter 7. Artificial intelligence for biodiversity conservation and wildlife protection - p. 109 8. Chapter 8. Computational intelligence for air pollution - p. 129 9. Chapter 9. Artificial intelligence-driven smart cities and sustainable urban development - p. 141 10. Chapter 10. Artificial intelligence for disaster prediction and mitigation - p. 155 11. Chapter 11. Artificial intelligence in carbon footprint reduction and energy efficiency - p. 169 12. Chapter 12. Artificial intelligence enhanced circular economy and sustainable business models - p. 181 13. Chapter 13. Computational sustainability: artificial intelligence in environmental policy and governance - p. 199 14. Chapter 14. Artificial intelligence-driven conservation of indigenous crops for advancing food security - p. 229 15. Chapter 15. Artificial intelligence and blockchain for sustainable farming: toward resilient and transparent agricultural ecosystems - p. 245 16. Chapter 16. Integrating artificial intelligence and computational intelligence for environmental protection and resource management - p. 259 17. Chapter 17. Ethical and regulatory challenges in artificial intelligence based unmanned air vehicles operations for environmental monitoring - p. 275 18. Index - p. 287
Climate change, biodiversity loss, resource depletion, and pollution are intensifying. Addressing these global issues requires fresh perspectives and cutting-edge solutions that integrate AI with environmental sustainability. AI and computational intelligence technologies are evolving at an unprecedented pace, offering new tools and methodologies for tackling environmental challenges. Yet existing resources often fail to capture the latest breakthroughs and interdisciplinary approaches needed to harness these innovations effectively.
AI and Computational Intelligence for Environmental Sustainability showcases the potential of artificial intelligence and computational intelligence in tackling critical environmental challenges. As the urgency to combat climate change, optimize resource utilization, and promote sustainable practices grows, this book provides a comprehensive resource that bridges the gap between AI research and its practical applications in environmental sustainability, and offers insights into the latest developments and methodologies. It highlights real-world applications and case studies to inspire researchers, developers, and policymakers to create and implement AI-driven solutions for a more sustainable future, foster collaboration by bringing together experts from diverse fields to share knowledge and foster interdisciplinary collaboration in the pursuit of environmental sustainability. It also addresses the ethical and practical challenges of applying AI in sustainability, advocating for responsible AI development and low-energy computing practices.
1. Chapter 1. Introduction to artificial intelligence for environmental sustainability - p. 1 2. Chapter 2. Artificial intelligence for climate modeling and prediction - p. 23 3. Chapter 3. Renewable energy optimization with artificial intelligence - p. 39 4. Chapter 4. Smart waste management and recycling using artificial intelligence - p. 63 5. Chapter 5. Artificial intelligence in water resource management - p. 73 6. Chapter 6. Integrated artificial intelligence-enabled precision agriculture system architecture - p. 87 7. Chapter 7. Artificial intelligence for biodiversity conservation and wildlife protection - p. 109 8. Chapter 8. Computational intelligence for air pollution - p. 129 9. Chapter 9. Artificial intelligence-driven smart cities and sustainable urban development - p. 141 10. Chapter 10. Artificial intelligence for disaster prediction and mitigation - p. 155 11. Chapter 11. Artificial intelligence in carbon footprint reduction and energy efficiency - p. 169 12. Chapter 12. Artificial intelligence enhanced circular economy and sustainable business models - p. 181 13. Chapter 13. Computational sustainability: artificial intelligence in environmental policy and governance - p. 199 14. Chapter 14. Artificial intelligence-driven conservation of indigenous crops for advancing food security - p. 229 15. Chapter 15. Artificial intelligence and blockchain for sustainable farming: toward resilient and transparent agricultural ecosystems - p. 245 16. Chapter 16. Integrating artificial intelligence and computational intelligence for environmental protection and resource management - p. 259 17. Chapter 17. Ethical and regulatory challenges in artificial intelligence based unmanned air vehicles operations for environmental monitoring - p. 275 18. Index - p. 287
