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Deep Learning for Hydrometeorology and Environmental Science

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...

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Main Authors: Lee, Taesam. (Author, http://id.loc.gov/vocabulary/relators/aut), Singh, Vijay P. (http://id.loc.gov/vocabulary/relators/aut), Cho, Kyung Hwa. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:1st ed. 2021.
Series:Water Science and Technology Library, 99
Subjects:
Water.
Artificial intelligence.
Environmental monitoring.
Environment.
Neural networks (Computer science) .
Environmental geography.
Water, general.
Artificial Intelligence.
Monitoring/Environmental Analysis.
Environment, general.
Mathematical Models of Cognitive Processes and Neural Networks.
Environmental Geography.
Online Access:https://doi.org/10.1007/978-3-030-64777-3

Internet

https://doi.org/10.1007/978-3-030-64777-3

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