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Deep learning for hydrometeorology a...

Lee, Taesam.

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Deep learning for hydrometeorology and environmental science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for hydrometeorology and environmental science/ by Taesam Lee, Vijay P. Singh, Kyung Hwa Cho.
Author:	Lee, Taesam.
other author:	Singh, Vijay P.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xiv, 204 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Hydrometeorology - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-030-64777-3
ISBN:	9783030647773

Deep learning for hydrometeorology and environmental science
Lee, Taesam.

Deep learning for hydrometeorology and environmental science[electronic resource] /by Taesam Lee, Vijay P. Singh, Kyung Hwa Cho. - Cham :Springer International Publishing :2021. - xiv, 204 p. :ill., digital ;24 cm. - Water science and technology library,v.990921-092X ;. - Water science and technology library ;v.99..

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.

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: 9783030647773

Standard No.: 10.1007/978-3-030-64777-3doiSubjects--Topical Terms:

3489585
Hydrometeorology
--Data processing.

LC Class. No.: GB2801.72.E45 / L448 2021

Dewey Class. No.: 551.570285

Deep learning for hydrometeorology and environmental science
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100 1 $a Lee, Taesam. $3 3489582
245 1 0 $a Deep learning for hydrometeorology and environmental science $h [electronic resource] / $c by Taesam Lee, Vijay P. Singh, Kyung Hwa Cho.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiv, 204 p. : $b ill., digital ; $c 24 cm.
490 1 $a Water science and technology library, $x 0921-092X ; $v v.99
505 0 $a 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.
520 $a 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.
650 0 $a Hydrometeorology $x Data processing. $3 3489585
650 0 $a Machine learning. $3 533906
650 0 $a Depth-area-duration (Hydrometeorology) $3 3489586
650 1 4 $a Water, general. $3 2056745
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Monitoring/Environmental Analysis. $3 3135580
650 2 4 $a Environment, general. $3 891227
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
650 2 4 $a Environmental Geography. $3 2131519
700 1 $a Singh, Vijay P. $3 1084587
700 1 $a Cho, Kyung Hwa. $3 3489583
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Water science and technology library ; $v v.99. $3 3489584
856 4 0 $u https://doi.org/10.1007/978-3-030-64777-3
950 $a Earth and Environmental Science (SpringerNature-11646)