Data Analytics and Artificial Intelligence for Earth Resource Management

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. Provides a comprehensive understanding of data analytics and artificial intelligence (AI) for earth resource managementIncludes real-world case studies and examples to demonstrate the practical applications of data analytics and AI in earth resource managementPresents clear illustrations, diagrams, and pictures that make the content more understandable and engaging INDICE: 1. Introduction to Data Analytics and Artificial Intelligence in Earth Resource Management2. Basics of Earth Resource Management3. Data Analytics for Earth Resource Management4. Artificial Intelligence for Earth Resource Management5. Data Preprocessing Techniques6. Analytics for Earth Resource Management7. Machine Learning for Earth Resource Management8. Deep Learning for Earth Resource Management9. Natural Language Processing for Earth Resource Management10. Remote Sensing and Geographic Information System for Earth Resource Management11. Case Studies12. Future Trends in Data Analytics and AI for Earth Resource Management

• ISBN: 978-0-443-23595-5
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 325
• Fecha Publicación: 01/11/2024
• Nº Volúmenes: 1
• Idioma: Inglés