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Artificial Intelligence in Earth Science
◆JpGU2025 セール開催中！：2025年6月30日(月)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Best Practices and Fundamental Challenges Author: Sun, Ziheng (Principal Investigator, Center for Spatial Information Science and Systems, George Mason University, USA) / Cristea, Nicoleta (Research scientist, Department of Civil and Environmental Engineering, University of Washington, USA) / Rivas, Pabl Publisher: Elsevier USA ISBN: 9780323917377 Cover: PAPERBACK Date: 2023年04月 DESCRIPTION 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. TABLE OF CONTENTS Copyright Contributors Chapter 1: Introduction of artificial intelligence in Earth sciences Abstract 1: Background and motivation 2: AI evolution in Earth sciences 3: Latest developments and challenges 4: Short-term and long-term expectations for AI 5: Future developments and how to adapt 6: Practical AI: From prototype to operation 7: Why do we write this book? 8: Learning goals and tasks 9: Assignments & open questions References Chapter 2: Machine learning for snow cover mapping Abstract 1: Introduction 2: Machine learning tools and model 3: Data preparation 4: Model parameter tuning 5: Model training 6: Model performance evaluation 7: Conclusion 8: Assignment 9: Open questions References Chapter 3: AI for sea ice forecasting Abstract 1: Introduction 2: Sea ice seasonal forecast 3: Sea ice data exploration 4: ML approaches for sea ice forecasting 5: Results and analysis 6: Discussion 7: Open questions 8: Assignments References Chapter 4: Deep learning for ocean mesoscale eddy detection Abstract Acknowledgments 1: Introduction 2: Chapter layout 3: Data preparation 4: Training and evaluating an eddy detection model 5: Discussion 6: Summary 7: Assignments 8: Open questions References Chapter 5: Artificial intelligence for plant disease recognition Abstract 1: Introduction 2: Data retrieval and preparation 3: Step-by-step implementation 4: Experimental results and how to select a model 5: Discussion 6: Conclusion 7: Assignment 8: Open questions References Chapter 6: Spatiotemporal attention ConvLSTM networks for predicting and physically interpreting wildfire spread Abstract 1: Introduction 2: Methodology 3: Earth AI workflow 4: Results 5: Conclusions 6: Assignment 7: Open questions References Chapter 7: AI for physics-inspired hydrology modeling Abstract 1: Introduction and background 2: PyTorch and autodifferentiation 3: Extremely brief background on numerical optimization 4: Bringing things together: Solving ODEs inside of neural networks 5: Scaling up to a conceptual hydrologic model 6: Conclusions References Further reading Chapter 8: Theory of spatiotemporal deep analogs and their application to solar forecasting Abstract 1: Introduction 2: Research data 3: Methodology 4: Results and discussion 5: Final remarks 6: Assignment 7: Open questions Appendix A: Deep learning layers and operators Appendix B: Verification of extended analog search with GFS Appendix C: Weather analog identification under a high irradiance regime Appendix D: Model attribution References Chapter 9: AI for improving ozone forecasting Abstract 1: Introduction 2: Background 3: Data collection 4: Dataset preparation 5: Machine learning 6: ML workflow management 7: Discussion 8: Conclusion 9: Assignment 10: Open questions 11: Lessons learned References Chapter 10: AI for monitoring power plant emissions from space Abstract 1: Introduction 2: Background 3: Data collection 4: Preprocessing 5: Machine learning 6: Managing emission AI workflow in Geoweaver 7: Discussion 8: Summary 9: Assignment 10: Open questions 11: Lessons learned References Chapter 11: AI for shrubland identification and mapping Abstract 1: Introduction 2: What you’ll learn 3: Background 4: Prerequisites 5: Model building 6: Discussion 7: Summary 8: Assignment 9: Open questions References Chapter 12: Explainable AI for understanding ML-derived vegetation products Abstract Acknowledgments 1: Introduction 2: Background 3: Prerequisites 4: Method & technique 5: Experiment & results 6: Summary 7: Assignment 8: Open questions 9: Lessons learned References Further reading Chapter 13: Satellite image classification using quantum machine learning Abstract Acknowledgment 1: Introduction 2: Data 3: Applying QML on MODIS hyperspectral images 4: Conclusions 5: Assignments 6: Open questions References Chapter 14: Provenance in earth AI Abstract Acknowledgments 1: Introduction 2: Overview of relevant concepts in provenance, XAI, and TAI 3: Need for provenance in earth AI 4: Technical approaches 5: Discussion 6: Conclusions 7: Assignment 8: Open questions References Chapter 15: AI ethics for earth sciences Abstract 1: Introduction 2: Prior work 3: Addressing ethical concerns during system design 4: Considerating algorithmic bias 5: Designing ethically driven automated systems 6: Assessing the impact of autonomous and intelligent systems on human well-being 7: Developing AI literacy, skills, and readiness 8: On documenting datasets for AI 9: On documenting AI models 10: Carbon emissions of earth AI models 11: Conclusions 12: Assignments 13: Open questions References Index
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