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Deep Learning for Hydrometeorology and Environmental Science (Paperback, 1st ed. 2021)
Loot Price: R3,246
Discovery Miles 32 460
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Deep Learning for Hydrometeorology and Environmental Science (Paperback, 1st ed. 2021)
Series: Water Science and Technology Library, 99
Expected to ship within 10 - 15 working days
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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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