Name: Deep Learning for Hydrometeorology and Environmental Science (en Inglés)
Price: 590519.00 COP
Availability: InStock
Author: Lee, Taesam ; Singh, Vijay P. ; Cho, Kyung Hwa
ISBN: 9783030647797

portada Deep Learning for Hydrometeorology and Environmental Science (en Inglés)

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

204

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 1.2 cm

Peso

0.31 kg.

ISBN13

9783030647797

Categorías

Hidrología e hidrosfera
Ciencia, ingeniería y tecnología medioambientales
Inteligencia artificial

Deep Learning for Hydrometeorology and Environmental Science (en Inglés)

Vijay P. Singh (Autor) · Taesam Lee (Autor) · Kyung Hwa Cho (Autor) · Springer · Tapa Blanda

Libro Físico

$ 590.519

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Ahorras: $ 393.679

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Deep Learning for Hydrometeorology and Environmental Science (en Inglés)"

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.