Electronic Resource

Deep Learning for Hydrometeorology and Environmental Science

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2108000159	EB0004078	553.7 Lee d	Central Library (OPAC)	Available

Detail Information

Series Title

Water Science and Technology Library

Call Number

553.7 Lee d

Publisher

Switzerland : Springer Cham., 2021

Collation

xiv, 204p.:Ill

Language

English

ISBN/ISSN

978-3-030-64777-3

Classification

553.7

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Water

Specific Detail Info

-

Statement of Responsibility

BRF

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment