Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography; pattern recognition for satellite images from remote sensing; management of agriculture and forests; assessment of climate change; and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics is covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms and deep learning, as well as the recent merging of machine learning and physics. End‑of‑chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data.
- Teaches both statistics and machine learning together, providing an integrated view of the two main components of data science
- Covers a broad range of data methods from correlation to deep neural networks, allowing students and researchers to choose the most appropriate tool to solve their particular data problem
- Presents the pro and cons of machine learning and statistical methods
- Covers the recent merging of machine learning and physics, two entirely different paradigms in science
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Reviews & endorsements
'As a new wave of machine learning becomes part of our toolbox for environmental science, this book is both a guide to the latest developments and a comprehensive textbook on statistics and data science. Almost everything is covered, from hypothesis testing to convolutional neural networks. The book is enjoyable to read, well explained and economically written, so it will probably become the first place I'll go to read up on any of these topics.' Alan Geer, European Centre for Medium-Range Weather Forecasts (ECMWF)
'William Hsieh has been one of the 'founding fathers' of an exciting new field of using machine learning (ML) in the environmental sciences. His new book provides readers with a solid introduction to the statistical foundation of ML and various ML techniques, as well as with the fundamentals of data science. The unique combination of solid mathematical and statistical backgrounds with modern applications of ML tools in the environmental sciences … is an important distinguishing feature of this book. The broad range of topics covered in this book makes it an invaluable reference and guide for researchers and graduate students working in this and related fields.' Vladimir Krasnopolsky, Center for Weather and Climate Prediction, NOAA
'Dr. Hsieh is one of the pioneers of the development of machine learning for the environmental sciences including the development of methods such as nonlinear principal component analysis to provide insights into the ENSO dynamic. Dr. Hsieh has a deep understanding of the foundations of statistics, machine learning, and environmental processes that he is sharing in this timely and comprehensive work with many recent references. It will no doubt become an indispensable reference for our field. I plan to use the book for my graduate environmental forecasting class and recommend the book for a self-guided progression or as a comprehensive reference.' Philippe Tissot, Texas A&M University-Corpus Christi
'There is a need for a forward-looking text on environmental data science and William Hsieh's text succeeds in filling the gap. This comprehensive text covers basic to advanced material ranging from timeless statistical techniques to some of the latest machine learning approaches. His refreshingly engaging style is written to be understood and complemented by a plethora of expressive visuals. Hsieh's treatment of nonlinearity is cutting-edge and the final chapter examines ways to combine machine learning with physics. This text is destined to become a modern classic.' Sue Ellen Haupt, National Center for Atmospheric Research
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Product details
- Date Published: March 2023
- format: Hardback
- isbn: 9781107065550
- length: 647 pages
- dimensions: 250 x 175 x 35 mm
- weight: 1.33kg
- availability: In stock
Table of Contents
1. Introduction
2. Basics
3. Probability distributions
4. Statistical inference
5. Linear regression
6. Neural networks
7. Nonlinear optimization
8. Learning and generalization
9. Principal components and canonical correlation
10. Unsupervised learning
11. Time series
12. Classification
13. Kernel methods
14. Decision trees, random forests and boosting
15. Deep learning
16. Forecast verification and post-processing
17. Merging of machine learning and physics
Appendices
References
Index.
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Author
William W. Hsieh, University of British Columbia, Vancouver
William W. Hsieh is a professor emeritus in the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia. Known as a pioneer in introducing machine learning to environmental science, he has written over 100 peer-reviewed journal papers on climate variability, machine learning, atmospheric science, oceanography, hydrology, and agricultural science. He is the author of the book Machine Learning Methods in the Environmental Sciences ( Cambridge University Press, 2009), the first single-authored textbook on machine learning for environmental scientists. Currently retired in Victoria, British Columbia, he enjoys growing organic vegetables.

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Introduction to Environmental Data Science

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