Deep learning for hydrometerology and environmental science / Taesam Lee, Vijay P. Singh, Kyung Hwa Cho.
2021
GB2801.72.E45 L44 2021
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Details
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
9783030647773 (electronic bk.)
3030647765
9783030647766
Published
Cham, Switzerland : Springer Nature, [2021]
Language
English
Description
1 online resource
Item Number
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.
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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).
Series
Water science and technology library ; v. 99.
Available in Other Form
Print version: 9783030647766
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Table of Contents
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.
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.