Deep learning for hydrometeorology and environmental science

著者

書誌事項

Deep learning for hydrometeorology and environmental science

Taesam Lee, Vijay P. Singh, Kyung Hwa Cho

(Water science and technology library, v. 99)

Springer, c2021

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内容説明・目次

内容説明

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.

目次

Chapter 1 Introduction 1.1 What is deep learning? 1.2 Pros and cons of deep learning 1.3 Recent applications of deep learning in hydrometeorological and environmental studies 1.4 Organization of chapters 1.5 Summary and conclusion Chapter 2 Mathematical Background 2.1 Linear regression model 2.2 Time series model 2.3 Probability distributions Chapter 3 Data Preprocessing 3.1 Normalization 3.2 Data splitting for training and testing Chapter 4 Neural Network 4.1 Terminology in neural network 4.2 Artificial neural network Chapter 5 . Training a Neural Network 5.1 Initialization 5.2 Gradient descent 5.3 Backpropagation Chapter 6 . Updating Weights 6.1 Momentum 6.2 Adagrad 6.3 RMSprop 6.4 Adam 6.5 Nadam 6.6 Python coding of updating weights Chapter 7 . Improving model performance 7.1 Batching and minibatch 7.2 Validation 7.3 Regularization Chapter 8 Advanced Neural Network Algorithms 8.1 Extreme Learning Machine (ELM) 8.2 Autoencoding Chapter 9 Deep learning for time series 9.1 Recurrent neural network 9.2 Long Short-Term Memory (LSTM) 9.3 Gated Recurrent Unit (GRU) Chapter 10 Deep learning for spatial datasets 10.1 Convolutional Neural Network (CNN) 10.2 Backpropagation of CNN Chapter 11 Tensorflow and Keras Programming for Deep Learning 11.1 Basic Keras modeling 11.2 Temporal deep learning (LSTM and GRU) 11.3 Spatial deep learning (CNN) Chapter 12 Hydrometeorological Applications of deep learning 12.1 Stochastic simulation with LSTM 12.2 Forecasting daily temperature with LSTM Chapter 13 Environmental Applications of deep learning 13.1 Remote sensing of water quality using CNN

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詳細情報

  • NII書誌ID(NCID)
    BC12608402
  • ISBN
    • 9783030647766
  • 出版国コード
    sz
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Cham
  • ページ数/冊数
    xiv, 204 p.
  • 大きさ
    25 cm
  • 親書誌ID
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