Understanding Atmospheric Rivers Using Machine Learning - Couverture souple
Livre 353 sur 472: SpringerBriefs in Applied Sciences and Technology
Synopsis
This book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). The chapters help understand how these climate phenomena influence AR behavior, offering a nuanced perspective on climate modeling and prediction.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Prof. Manish Kumar Goyal is a Chair professor- BIS Standardization and Dean, Infrastructure Development at Indian Institute of Technology Indore. His research interests include water resources engineering, GIS, and remote sensing applications in water and environment and climate change. He received a B.Tech. degree in Civil Engineering from the National Institute of Technology Warangal with distinction and an M.Tech. degree from the Indian Institute of Technology Roorkee. After a brief stint in corporate, he pursued a Ph.D. degree at IIT Roorkee in collaboration with the University of Waterloo, Canada. He went on to pursue further research as a postdoctoral fellow at Nanyang Technological University, Singapore, and McGill University, Canada. He holds more than 100 publications in different domains of GIS and Remote Sensing, Water Resources, Climate Change, Hydrological and Hydrodynamic Modeling, Snow and Glacier Melt, Soil Carbon Sequestration, Anthropogenic Changes, Risk, and Resilience.His name has appeared in Top 2% scientist, list prepared by Stanford University in 2020, 2021, 2022 and 2023.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Goyal:Climate Extreme and Atmospheric R (eng)
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Understanding Atmospheric Rivers Using Machine Learning
Edité par
Springer, Berlin, Springer Nature Switzerland, Springer, 2024
ISBN 10 : 3031634772
ISBN 13 : 9783031634772
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). It begins with the historical background and mechanisms governing AR formation, giving insights into the global and regional perspectives of ARs, observing their varying manifestations across different geographical contexts. The book explores the key characteristics of ARs, from their frequency and duration to intensity, unraveling the intricate relationship between atmospheric rivers and precipitation. The book also focus on the intersection of ARs with large-scale climate oscillations, such as El Niño and La Niña events, the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO). The chapters help understand how these climate phenomena influence AR behavior, offering a nuanced perspective on climate modeling and prediction. The book also covers artificial intelligence (AI) applications, from pattern recognition to prediction modeling and early warning systems. A case study on AR prediction using deep learning models exemplifies the practical applications of AI in this domain. The book culminates by underscoring the interdisciplinary nature of AR research and the synergy between atmospheric science, climatology, and artificial intelligence 74 pp. Englisch. N° de réf. du vendeur 9783031634772
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Understanding Atmospheric Rivers Using Machine Learning (SpringerBriefs in Applied Sciences and Technology)
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Understanding Atmospheric Rivers Using Machine Learning (SpringerBriefs in Applied Sciences and Technology)
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Climate Extreme and Atmospheric Rivers with Machine Learning
Edité par
Springer, Berlin|Springer Nature Switzerland|Springer, 2024
ISBN 10 : 3031634772
ISBN 13 : 9783031634772
Neuf
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). It begins with the historical background and mechanisms governing AR formation, giving insights into the global and regional perspectives of ARs,. N° de réf. du vendeur 1669554889
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Understanding Atmospheric Rivers Using Machine Learning
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Springer, Springer Nature Switzerland Jun 2024, 2024
ISBN 10 : 3031634772
ISBN 13 : 9783031634772
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). It begins with the historical background and mechanisms governing AR formation, giving insights into the global and regional perspectives of ARs, observing their varying manifestations across different geographical contexts. The book explores the key characteristics of ARs, from their frequency and duration to intensity, unraveling the intricate relationship between atmospheric rivers and precipitation. The book also focus on the intersection of ARs with large-scale climate oscillations, such as El Niño and La Niña events, the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO). The chapters help understand how these climate phenomena influence AR behavior, offering a nuanced perspective on climate modeling and prediction. The book also covers artificial intelligence (AI) applications, from pattern recognition to prediction modeling and early warning systems. A case study on AR prediction using deep learning models exemplifies the practical applications of AI in this domain. The book culminates by underscoring the interdisciplinary nature of AR research and the synergy between atmospheric science, climatology, and artificial intelligenceSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 84 pp. Englisch. N° de réf. du vendeur 9783031634772
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Understanding Atmospheric Rivers Using Machine Learning
Edité par
Springer Nature Switzerland, 2024
ISBN 10 : 3031634772
ISBN 13 : 9783031634772
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). It begins with the historical background and mechanisms governing AR formation, giving insights into the global and regional perspectives of ARs, observing their varying manifestations across different geographical contexts. The book explores the key characteristics of ARs, from their frequency and duration to intensity, unraveling the intricate relationship between atmospheric rivers and precipitation. The book also focus on the intersection of ARs with large-scale climate oscillations, such as El Niño and La Niña events, the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO). The chapters help understand how these climate phenomena influence AR behavior, offering a nuanced perspective on climate modeling and prediction. The book also covers artificial intelligence (AI) applications, from pattern recognition to prediction modeling and early warning systems. A case study on AR prediction using deep learning models exemplifies the practical applications of AI in this domain. The book culminates by underscoring the interdisciplinary nature of AR research and the synergy between atmospheric science, climatology, and artificial intelligence. N° de réf. du vendeur 9783031634772
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Understanding Atmospheric Rivers Using Machine Learning
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Taschenbuch. Etat : Neu. Understanding Atmospheric Rivers Using Machine Learning | Manish Kumar Goyal (u. a.) | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | viii | Englisch | 2024 | Springer | EAN 9783031634772 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 129292027
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Understanding Atmospheric Rivers Using Machine Learning
Edité par
Springer, 2024
ISBN 10 : 3031634772
ISBN 13 : 9783031634772
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Couverture souple
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Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). It begins with the historical background and mechanisms governing AR formation, giving insights into the global and regional perspectives of ARs, observing their varying manifestations across different geographical contexts. The book explores the key characteristics of ARs, from their frequency and duration to intensity, unraveling the intricate relationship between atmospheric rivers and precipitation. The book also focus on the intersection of ARs with large-scale climate oscillations, such as El Niño and La Niña events, the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO). The chapters help understand how these climate phenomena influence AR behavior, offering a nuanced perspective on climate modeling and prediction. The book also covers artificial intelligence (AI) applications, from pattern recognition to prediction modeling and early warning systems. A case study on AR prediction using deep learning models exemplifies the practical applications of AI in this domain. The book culminates by underscoring the interdisciplinary nature of AR research and the synergy between atmospheric science, climatology, and artificial intelligence. N° de réf. du vendeur 43045881/1
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