Deep Learning for Hydrometeorology and Environmental Science
Vijay P Singh author Taesam Lee author Kyung Hwa Cho author
Format:Paperback
Publisher:Springer Nature Switzerland AG
Published:28th Jan '22
Currently unavailable, and unfortunately no date known when it will be back
This paperback is available in another edition too:
- Hardback£99.99(9783030647766)
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.
ISBN: 9783030647797
Dimensions: unknown
Weight: 343g
204 pages
1st ed. 2021