| |  Online-Ressource |
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| Verfasst von: | Lee, Taesam [VerfasserIn]  |
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| | Singh, Vijay P. [VerfasserIn] |
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| | Cho, Kyung Hwa [VerfasserIn] |
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| Titel: | Deep Learning for Hydrometeorology and Environmental Science |
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| Verf.angabe: | by Taesam Lee, Vijay P. Singh, Kyung Hwa Cho |
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| Ausgabe: | 1st ed. 2021. |
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| Verlagsort: | Cham |
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| | Cham |
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| Verlag: | Springer International Publishing |
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| | Imprint: Springer |
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| E-Jahr: | 2021 |
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| Jahr: | 2021. |
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| | 2021. |
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| Umfang: | 1 Online-Ressource(XIV, 204 p. 189 illus., 133 illus. in color.) |
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| Gesamttitel/Reihe: | Water Science and Technology Library ; 99 |
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| | Springer eBook Collection |
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| ISBN: | 978-3-030-64777-3 |
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| Abstract: | Introduction -- Mathematical Background -- Data Preprocessing -- Neural Network -- Training a Neural Network -- Updating Weights -- Improving model performance -- Advanced Neural Network Algorithms -- Deep learning for time series -- Deep learning for spatial datasets -- Tensorflow and Keras Programming for Deep Learning -- Hydrometeorological Applications of deep learning -- Environmental Applications of deep learning. |
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| | 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. |
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| DOI: | doi:10.1007/978-3-030-64777-3 |
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| URL: | Resolving-System: https://doi.org/10.1007/978-3-030-64777-3 |
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| | DOI: https://doi.org/10.1007/978-3-030-64777-3 |
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| Schlagwörter: | (s)Hydrometeorologie / (s)Hydrologie / (s)Maschinelles Lernen / (s)Angewandte Mathematik |
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| | (s)Wasserhaushalt / (s)Prognose / (s)Neuronales Netz / (s)Modellierung / (s)Mathematisches Modell |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Bibliogr. Hinweis: | Erscheint auch als : Druck-Ausgabe |
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| | Erscheint auch als : Druck-Ausgabe |
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| | Erscheint auch als : Druck-Ausgabe |
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| K10plus-PPN: | 1746360992 |
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| Lokale URL UB: |  Zum Volltext |
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Deep Learning for Hydrometeorology and Environmental Science / Lee, Taesam [VerfasserIn]; 2021. (Online-Ressource)
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