Lee taesam (39 resultados)
Idioma: Inglés
Editorial: Springer 2021
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Idioma: Inglés
Editorial: Springer 2021
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Idioma: Inglés
Editorial: Springer 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Hardcover. Condición: new. Hardcover. Global climate change is typically understood and modeled using global climate models (GCMs), but the outputs of these models in terms of hydrological variables are only available on coarse or large spatial and time scales, while finer spatial and temporal resolutions are needed to reliably…assess the hydro-environmental impacts of climate change. To reliably obtain the required resolutions of hydrological variables, statistical downscaling is typically employed. Statistical Downscaling for Hydrological and Environmental Applications presents statistical downscaling techniques in a practical manner so that both students and practitioners can readily utilize them. Numerous methods are presented, and all are illustrated with practical examples. The book is written so that no prior background in statistics is needed, and it will be useful to graduate students, college faculty, and researchers in hydrology, hydroclimatology, agricultural and environmental sciences, and watershed management. It will also be of interest to environmental policymakers at the local, state, and national levels, as well as readers interested in climate change and its related hydrologic impacts.Features: Examines how to model hydrological events such as extreme rainfall, floods, and droughts at the local, watershed level. Explains how to properly correct for significant biases with the observational data normally found in current Global Climate Models (GCMs). Presents temporal downscaling from daily to hourly with a nonparametric approach. Discusses the myriad effects of climate change on hydrological processes. This book presents statistical downscaling techniques in a practical manner so that readers can easily adopt the techniques for hydrological applications and designs in response to climate change. It also provides numerous examples and background information on reliability of impact assessments of climate change and what the results imply. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Idioma: Inglés
Editorial: Springer 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Más imágenesIdioma: Inglés
Editorial: Springer 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: Neu. Deep Learning for Hydrometeorology and Environmental Science | Taesam Lee (u. a.) | Taschenbuch | Water Science and Technology Library | xiv | Englisch | 2022 | Springer | EAN 9783030647797 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]har…tmann[at]springer[dot]com | Anbieter: preigu.
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Deep Learning for Hydrometeorology and Environmental Science
Lee, Taesam (Author)/ Singh, Vijay P. (Author)/ Cho, Kyung Hwa (Author)
Idioma: Inglés
Editorial: Springer 2021
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: Revaluation Books, Exeter, , Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 218 pages. 9.25x6.10x0.67 inches. In Stock.
Idioma: Inglés
Editorial: Springer International Publishing 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with rea…l datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited.Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Idioma: Inglés
Editorial: Springer International Publishing 2021
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datas…ets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited.Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Condición: New.
Idioma: Inglés
Editorial: Springer 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: Buchpark, Trebbin, , AlemaniaBuchpark
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Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples…with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited.Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Idioma: Inglés
Editorial: Springer 2021
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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Hardcover. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Hardcover. Condición: Brand New. 161 pages. 9.25x6.25x0.75 inches. In Stock.
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Hardcover. Condición: new. Hardcover. Global climate change is typically understood and modeled using global climate models (GCMs), but the outputs of these models in terms of hydrological variables are only available on coarse or large spatial and time scales, while finer spatial and temporal resolutions are needed to reliably…assess the hydro-environmental impacts of climate change. To reliably obtain the required resolutions of hydrological variables, statistical downscaling is typically employed. Statistical Downscaling for Hydrological and Environmental Applications presents statistical downscaling techniques in a practical manner so that both students and practitioners can readily utilize them. Numerous methods are presented, and all are illustrated with practical examples. The book is written so that no prior background in statistics is needed, and it will be useful to graduate students, college faculty, and researchers in hydrology, hydroclimatology, agricultural and environmental sciences, and watershed management. It will also be of interest to environmental policymakers at the local, state, and national levels, as well as readers interested in climate change and its related hydrologic impacts.Features: Examines how to model hydrological events such as extreme rainfall, floods, and droughts at the local, watershed level. Explains how to properly correct for significant biases with the observational data normally found in current Global Climate Models (GCMs). Presents temporal downscaling from daily to hourly with a nonparametric approach. Discusses the myriad effects of climate change on hydrological processes. This book presents statistical downscaling techniques in a practical manner so that readers can easily adopt the techniques for hydrological applications and designs in response to climate change. It also provides numerous examples and background information on reliability of impact assessments of climate change and what the results imply. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Idioma: Inglés
Editorial: Springer 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Condición: new. Questo è un articolo print on demand.
Idioma: Inglés
Editorial: Springer 2021
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Condición: new. Questo è un articolo print on demand.
Idioma: Inglés
Editorial: Springer International Publishing Jan 2022 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as e…xamples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited.Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model. 220 pp. Englisch.
Idioma: Inglés
Editorial: Springer International Publishing Jan 2021 2021
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples… with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited.Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model. 220 pp. Englisch.
Idioma: Inglés
Editorial: Springer, Berlin|Springer International Publishing|Springer 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: moluna, Greven, , Alemaniamoluna
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EUR 109,83
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parame…ters, with back-propagation as well as examples with real dataset.
Idioma: Inglés
Editorial: Springer International Publishing 2021
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: moluna, Greven, , Alemaniamoluna
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides step-by-step tutorials that help the reader to learn complex deep learning algorithmsGives an explanation of deep learning techniques and their applications to hydrometeorological and environmental studies Co…vers major deep le.
Idioma: Inglés
Editorial: Springer 2022
Serie: Water Science and Technology Library, Libro 88 de 101. Libro 88 de 101 - Water Science and Technology Library
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Condición: New. PRINT ON DEMAND.
