Deep learning for hydrometerology and environmental science / Taesam Lee, Vijay P. Singh, Kyung Hwa Cho.
Lee, Taesam, author.; Singh, V. P. (Vijay P.), author.; Cho, Kyung Hwa, author.
2021
GB2801.72.E45 L44 2021
Available Online

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Title Deep learning for hydrometerology and environmental science / Taesam Lee, Vijay P. Singh, Kyung Hwa Cho.
Author Lee, Taesam, author.
ISBN 3030647773 (electronic book)
9783030647773 (electronic bk.)
3030647765
9783030647766
DOI https://doi.org/10.1007/978-3-030-64777-3
Published Cham, Switzerland : Springer Nature, [2021]
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-3-030-64777-3 doi
Call Number GB2801.72.E45 L44 2021
Dewey Decimal Classification 551.570285
Summary 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).
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from digital title page (viewed on February 11, 2021).
Added Author Singh, V. P. (Vijay P.), author.
Cho, Kyung Hwa, author.
Series Water science and technology library ; v. 99.
Available in Other Form Print version: 9783030647766
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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.

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