| 書目名稱 | Deep Learning for Hydrometeorology and Environmental Science |
| 編輯 | Taesam Lee,Vijay P. Singh,Kyung Hwa Cho |
| 視頻video | http://file.papertrans.cn/265/264608/264608.mp4 |
| 概述 | Provides step-by-step tutorials that help the reader to learn complex deep learning algorithms.Gives an explanation of deep learning techniques and their applications to hydrometeorological and enviro |
| 叢書名稱 | Water Science and Technology Library |
| 圖書封面 |  |
| 描述 | .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 Convo |
| 出版日期 | Book 2021 |
| 關(guān)鍵詞 | Hydrology; Meteorology; Artificial neural networks; Climate index; Convolutional neural networks; Lon Sho |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-64777-3 |
| isbn_softcover | 978-3-030-64779-7 |
| isbn_ebook | 978-3-030-64777-3Series ISSN 0921-092X Series E-ISSN 1872-4663 |
| issn_series | 0921-092X |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機版|小黑屋|
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