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: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom
Cambridge University Press
2023
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| Schlagworte: | |
| Online-Zugang: | DE-12 DE-634 DE-92 DE-91 DE-19 URL des Erstveröffentlichers |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 Online-Ressource (xx, 627 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781107588493 |
| DOI: | 10.1017/9781107588493 |
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| discipline | Biologie Informatik Soziologie Umwelt |
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| index_date | 2024-07-03T21:57:47Z |
| indexdate | 2025-02-13T09:00:48Z |
| institution | BVB |
| isbn | 9781107588493 |
| language | English |
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| spelling | Hsieh, William Wei 1955- (DE-588)128566373X aut Introduction to environmental data science William W. Hsieh Cambridge, United Kingdom Cambridge University Press 2023 1 Online-Ressource (xx, 627 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier 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. Environmental sciences / Data processing Environmental protection / Data processing Environmental management / Data processing Machine learning Klimaänderung (DE-588)4164199-1 gnd rswk-swf Deep Learning (DE-588)1135597375 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Umweltwissenschaften (DE-588)4137364-9 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Deep Learning (DE-588)1135597375 s Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s Umweltwissenschaften (DE-588)4137364-9 s Klimaänderung (DE-588)4164199-1 s DE-604 Data Science (DE-588)1140936166 s Erscheint auch als Druck-Ausgabe 978-1-107-06555-0 https://doi.org/10.1017/9781107588493 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Hsieh, William Wei 1955- Introduction to environmental data science Environmental sciences / Data processing Environmental protection / Data processing Environmental management / Data processing Machine learning Klimaänderung (DE-588)4164199-1 gnd Deep Learning (DE-588)1135597375 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Umweltwissenschaften (DE-588)4137364-9 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
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| title | Introduction to environmental data science |
| title_auth | Introduction to environmental data science |
| title_exact_search | Introduction to environmental data science |
| title_exact_search_txtP | Introduction to environmental data science |
| title_full | Introduction to environmental data science William W. Hsieh |
| title_fullStr | Introduction to environmental data science William W. Hsieh |
| title_full_unstemmed | Introduction to environmental data science William W. Hsieh |
| title_short | Introduction to environmental data science |
| title_sort | introduction to environmental data science |
| topic | Environmental sciences / Data processing Environmental protection / Data processing Environmental management / Data processing Machine learning Klimaänderung (DE-588)4164199-1 gnd Deep Learning (DE-588)1135597375 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Umweltwissenschaften (DE-588)4137364-9 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Environmental sciences / Data processing Environmental protection / Data processing Environmental management / Data processing Machine learning Klimaänderung Deep Learning Datenanalyse Data Science Umweltwissenschaften Statistik Maschinelles Lernen |
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