Overview

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.

Full Product Details

Author:   Ziheng Sun (Principal Investigator, Center for Spatial Information Science and Systems, George Mason University, USA) ,  Nicoleta Cristea (Research scientist, Department of Civil and Environmental Engineering, University of Washington, USA) ,  Pablo Rivas (Assistant Professor of Computer Science, Baylor University, USA)
Publisher:   Elsevier - Health Sciences Division
Imprint:   Elsevier - Health Sciences Division
Weight:   1.000kg
ISBN:  

9780323917377

ISBN 10:   0323917372
Pages:   430
Publication Date:   26 April 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Part I: Fundamentals of Earth AI 1. Basic Concepts of Earth AI 2. Introductory AI Algorithms 3. AI Infrastructure - hardware and software for developing Earth AI Part II. Existing Best Practices 4. AI for Earthquake Hidden Signal Detection 5. AI for Dust Storm Detection 6. AI for Snow Monitoring 7. AI for Volcano Pre-warning and Prediction 8. AI for Landslide Damage Assessment 9. AI for Hurricane Prediction 10. AI for Precipitation Prediction 11. AI for Drought Monitoring 12. AI for Wildfire Detection 13. AI for Air Quality Prediction 14. AI for Agricultural Irrigation Decision Making 15. AI for Land Cover Land Use Classification 16. AI for Ocean mesoscale eddies detection Part III Fundamental Challenges for AI in Earth Sciences 17. AI Model Selection and Tuning 18. Training Data Preparation 19. Explainable AI 20. AI Generalization 21. AI Integration with Physics-based Models 22. AI Provenance (Replicability & Reproducibility) 23. AI Ethics

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Author Information

Ziheng Sun is a Principal Investigator at the Center for Spatial Information Science and Systems, and a research assistant professor the Department of Geography and Geoinformation Science at George Mason University. He is a practitioner of using the latest technologies such as artificial intelligence and high-performance computing, to seek for answers to the questions in geoscience. He invented RSSI, a novel index for artificial object recognition from high resolution aerial images, and proposed parameterless automatic classification solution for reducing the parameter-tuning burden on scientists. Prof Sun has published over 50 papers in renowned journals in geoscience and has worked on several federal-funded projects to build geospatial cyberinfrastructure systems for better disseminating, processing, visualizing, and understanding spatial big data. Nicoleta Cristea is a research scientist in the Department of Civil and Environmental Engineering at the University of Washington (UW), a research scientist with the UW Freshwater Initiative, and a data science fellow at the UW eScience Institute. Her current research focus is on modeling snow surface temperature and evaluating spatially distributed hydrologic models. Nicoleta is currently leading an NSF-funded project on mapping snow covered areas from Cubesat imagery using deep learning techniques. Pablo Rivas is assistant professor of computer science at Baylor University where he teaches courses related to machine learning, deep learning, data mining, and theory. His research areas include deep machine learning and large-scale data mining in big data analytics, large-scale multidimensional multispectral signal analysis, statistical pattern recognition methods, image restoration, image analysis, intelligent software systems, and health-care imaging. Other research areas include applied mathematics, numerical optimization, swarm intelligence optimization, evolutionary algorithms, soft computing, fuzzy logic, neural networks, and neuro-fuzzy systems.

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