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Deep learning for hydrometeorology a...

Lee, Taesam.

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Deep learning for hydrometeorology and environmental science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning for hydrometeorology and environmental science/ by Taesam Lee, Vijay P. Singh, Kyung Hwa Cho.
作者:	Lee, Taesam.
其他作者:	Singh, Vijay P.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xiv, 204 p. :ill., digital ;24 cm.
內容註:	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
標題:	Hydrometeorology - Data processing. -
電子資源:	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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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.
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