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.- Provides practical, step-by-step guides for Earth Scientists who are interested in implementing AI techniques in their work- Features case studies to show real-world examples of techniques described in the book- Includes additional elements to help readers who are new to AI, including end-of-chapter, key concept bulleted lists that concisely cover key concepts in the chapter
Table of Contents
1. Introduction of artificial intelligence in Earth sciences2. Machine learning for snow cover mapping3. AI for sea ice forecasting4. Deep learning for ocean mesoscale eddy detection5. Artificial intelligence for plant disease recognition6. Spatiotemporal attention ConvLSTM networks for predicting and physically interpreting wildfire spread7. AI for physics-inspired hydrology modeling8. Theory of spatiotemporal deep analogs and their application to solar forecasting9. AI for improving ozone forecasting10. AI for monitoring power plant emissions from space11. AI for shrubland identification and mapping12. Explainable AI for understanding ML-derived vegetation products13. Satellite image classification using quantum machine learning14. Provenance in earth AI15. AI ethics for earth sciences
