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Introduction to Environmental Data Science

Introduction to Environmental Data Science

$79.99 (P)

  • Publication planned for: March 2023
  • availability: Not yet published - available from March 2023
  • format: Hardback
  • isbn: 9781107065550

$ 79.99 (P)
Hardback

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  • 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 are 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 data sets 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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    Product details

    • Publication planned for: March 2023
    • format: Hardback
    • isbn: 9781107065550
    • length: 500 pages
    • dimensions: 244 x 170 mm
    • availability: Not yet published - available from March 2023
  • 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.

  • 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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