Téléchargez le livre : Deep Learning for Hydrometeorology and Environmental Science

Deep Learning for Hydrometeorology and Environmental Science

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Éditeur :

Springer


Collection :

Water Science and Technology Library

Paru le : 2021-01-27

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Description

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.
Pages
204 pages
Collection
Water Science and Technology Library
Parution
2021-01-27
Marque
Springer
EAN papier
9783030647766
EAN PDF
9783030647773
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
20
Taille du fichier
10629 Ko
Prix
116,04 €
EAN EPUB
9783030647773
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
20
Taille du fichier
56893 Ko
Prix
116,04 €

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