Deep learning for hydrometeorology and environmental science
著者
書誌事項
Deep learning for hydrometeorology and environmental science
(Water science and technology library, v. 99)
Springer, c2021
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
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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