國立高雄大學圖資館 |

Language: English

Back

Deep learning for hydrometeorology a...

Cho, Kyung Hwa.

Deep learning for hydrometeorology and environmental science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for hydrometeorology and environmental scienceby 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.
Contained By:	Springer Nature eBook
Subject:	HydrometeorologyData processing.
Online resource:	https://doi.org/10.1007/978-3-030-64777-3
ISBN:	9783030647773$q(electronic bk.)

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.66..

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$q(electronic bk.)

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

890046
Hydrometeorology
--Data processing.

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

Dewey Class. No.: 551.570285

Deep learning for hydrometeorology and environmental science
LDR:02815nmm a2200337 a 4500 001 596989
003 DE-He213
005 20210625091906.0
006 m d
007 cr nn 008maaau
008 211019s2021 sz s 0 eng d
020 $a 9783030647773$q(electronic bk.)
020 $a 9783030647766$q(paper)
024 7 $a 10.1007/978-3-030-64777-3 $2 doi
035 $a 978-3-030-64777-3
040 $a GP $c GP
041 0 $a eng
050 4 $a GB2801.72.E45 $b L448 2021
072 7 $a RBK $2 bicssc
072 7 $a SCI081000 $2 bisacsh
072 7 $a RBK $2 thema
082 0 4 $a 551.570285 $2 23
090 $a GB2801.72.E45 $b L477 2021
100 1 $a Lee, Taesam. $3 890044
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 890046
650 0 $a Machine learning. $3 188639
650 0 $a Depth-area-duration (Hydrometeorology) $3 890047
650 1 4 $a Water, general. $3 676976
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Monitoring/Environmental Analysis. $3 759863
650 2 4 $a Environment, general. $3 273722
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
650 2 4 $a Environmental Geography. $3 711703
700 1 $a Singh, Vijay P. $3 484004
700 1 $a Cho, Kyung Hwa. $3 890045
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Water science and technology library ; $v v.66. $3 558569
856 4 0 $u https://doi.org/10.1007/978-3-030-64777-3
950 $a Earth and Environmental Science (SpringerNature-11646)