update book details - Artificial Intelligence in Earth Science

content/english/publications/_index.md

@@ -21,15 +21,14 @@ researchPapers:
     authors: "Sun, Ziheng | Cristea, Nicoleta C. | Yang, Kehan | Alnuaim, Ahmed | Bikshapathireddy, Lakshmi Chetana Gomaram | John, Aji | Pflug, Justin | Li, Brian | Pan, Hailey | Shyamsunder, Nikil | Reddygari, Rithvik | Bhandaru, Praneeth"
     type: "Research Paper"
 
-
 books:
-  - title: "Actionable Science of Global Environment Change"
+  - title: "Artificial Intelligence in Earth Science"
     chicagoCite: ""
-    authors: "Ziheng Sun | Bhargavi Janga | Gokul Prathin Asamani | Qian Huang | Hui Fang | Elia Axinia Machado | Diego F. Cuadros | Tao Hu | Xiao Huang | Siqin Wang"
-    image: "https://media.springernature.com/full/springer-static/cover-hires/book/978-3-031-41758-0?as=webp"
-    bookUrl: "https://link.springer.com/book/10.1007/978-3-031-41758-0"
-    aboutBook: "This volume teaches readers how to sort through the vast mountain of climate and environmental science data to extract actionable insights. With the advancements in sensing technology, we now observe petabytes of data related to climate and the environment. While the volume of data is impressive, collecting big data for the sake of data alone proves to be of limited utility. Instead, our quest is for actionable data that can drive tangible actions and meaningful impact. Yet, unearthing actionable insights from the accumulated big data and delivering them to global stakeholders remains a burgeoning field. Although traditional data mining struggles to keep pace with data accumulation, scientific evolution has spurred the emergence of new technologies like numeric modeling and machine learning. These cutting-edge tools are now tackling grand challenges in climate and the environment, from forecasting extreme climate events and enhancing environmental productivity to monitoringgreenhouse gas emissions, fostering smart environmental solutions, and understanding aerosols. Additionally, they model environmental-human interactions, inform policy, and steer markets towards a healthier and more environment-friendly direction. While there's no universal solution to address all these formidable tasks, this book takes us on a guided journey through three sections, enriched with chapters from domain scientists. Part I defines actionable science and explores what truly renders data actionable. Part II showcases compelling case studies and practical use scenarios, illustrating these principles in action. Finally, Part III provides an insightful glimpse into the future of actionable science, focusing on the pressing climate and environmental issues we must confront. Embark on this illuminating voyage with us, where big data meets practical research, and discover how our collective efforts move us closer to a sustainable and thriving future. This book is an invitation to unlock the mysteries of our environment, transforming data into decisive action for generations to come."
-    aboutBookReadMoreUrl: "https://link.springer.com/book/10.1007/978-3-031-41758-0#about-this-book"
+    editors: "Ziheng Sun | Nicoleta Cristea |  Pablo Rivas"
+    image: "https://secure-ecsd.elsevier.com/covers/80/Tango2/large/9780323917377.jpg"
+    bookUrl: "https://shop.elsevier.com/books/artificial-intelligence-in-earth-science/sun/978-0-323-91737-7"
+    aboutBook: "Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth Science. The book focuses on the most challenging problems in applying AI in Earth system sciences, such as training data preparation, model selection, hyperparameter tuning, model structure optimization, spatiotemporal generalization, transforming model results into products, and explaining trained models. In addition, it provides full-stack workflow tutorials to help walk readers through the whole process, regardless of previous AI experience. The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work."
+    aboutBookReadMoreUrl: "https://shop.elsevier.com/books/artificial-intelligence-in-earth-science/sun/978-0-323-91737-7"
     type: "Book" 
 
 articles:

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@@ -38,7 +38,7 @@ <h4>Books</h4>
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                 <p class="mt-5 font-heebo font-bold">About the Book: </p>