Introduction to environmental data science /
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...
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| Format: | Book |
| Language: | English |
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Cambridge ; New York :
Cambridge University Press,
2023.
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| Summary: | 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 datasets allow readers to practice analysis of real data. |
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| Physical Description: | xx, 625 pages : illustrations ; 25 cm. |
| Bibliography: | Includes bibliographical references (pages 573-611) and index. |
| ISBN: | 9781107065550 1107065550 |