Deep Learning for Hydrometeorology and Environmental Science Paperback / softback
by Taesam Lee, Vijay P. Singh, Kyung Hwa Cho
Part of the Water Science and Technology Library series
Paperback / softback
Description
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.
Information
-
Out of stock
- Format:Paperback / softback
- Pages:204 pages, 133 Illustrations, color; 56 Illustrations, black and white; XIV, 204 p. 189 illus., 133
- Publisher:Springer Nature Switzerland AG
- Publication Date:28/01/2022
- Category:
- ISBN:9783030647797
Other Formats
- Hardback from £99.99
Information
-
Out of stock
- Format:Paperback / softback
- Pages:204 pages, 133 Illustrations, color; 56 Illustrations, black and white; XIV, 204 p. 189 illus., 133
- Publisher:Springer Nature Switzerland AG
- Publication Date:28/01/2022
- Category:
- ISBN:9783030647797
