Taesam Lee,
Vijay P. Singh,
Kyung Hwa Cho
Deep Learning for Hydrometeorology and Environmental Science
Paperback Engels 2022 9783030647797
Verwachte levertijd ongeveer 9 werkdagen
Samenvatting
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.
Specificaties
ISBN13:9783030647797
Taal:Engels
Bindwijze:paperback
Uitgever:Springer International Publishing
Lezersrecensies
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Inhoudsopgave
<p>Chapter 1 Introduction</p>
<p>1.1 What is deep learning?</p>
<p>1.2 Pros and cons of deep learning</p>
<p>1.3 Recent applications of deep learning in hydrometeorological and environmental studies</p>
<p>1.4 Organization of chapters</p>
<p>1.5 Summary and conclusion</p>
<p>Chapter 2 Mathematical Background</p>
<p>2.1 Linear regression model</p>
<p>2.2 Time series model</p>
<p>2.3 Probability distributions</p>
<p>Chapter 3 Data Preprocessing</p>
<p>3.1 Normalization</p>
<p>3.2 Data splitting for training and testing</p>
<p>Chapter 4 Neural Network</p>
<p>4.1 Terminology in neural network</p>
<p>4.2 Artificial neural network</p>
<p>Chapter 5 . Training a Neural Network</p>
<p>5.1 Initialization</p>
<p>5.2 Gradient descent</p>
<p>5.3 Backpropagation</p>
<p>Chapter 6 . Updating Weights</p>
<p>6.1 Momentum</p>
<p>6.2 Adagrad</p>
<p>6.3 RMSprop</p>
<p>6.4 Adam</p>
<p>6.5 Nadam</p>
<p>6.6 Python coding of updating weights</p>
<p>Chapter 7 . Improving model performance</p>
<p>7.1 Batching and minibatch</p>
<p>7.2 Validation</p>
<p>7.3 Regularization</p>
<p>Chapter 8 Advanced Neural Network Algorithms</p>
<p>8.1 Extreme Learning Machine (ELM)</p>
<p>8.2 Autoencoding</p>
<p>Chapter 9 Deep learning for time series</p>
<p>9.1 Recurrent neural network</p>
<p>9.2 Long Short-Term Memory (LSTM)</p>
<p>9.3 Gated Recurrent Unit (GRU)</p>
<p>Chapter 10 Deep learning for spatial datasets</p>
<p>10.1 Convolutional Neural Network (CNN)</p>
10.2 Backpropagation of CNN<p></p>
<p>Chapter 11 Tensorflow and Keras Programming for Deep Learning</p>
<p>11.1 Basic Keras modeling</p>
<p>11.2 Temporal deep learning (LSTM and GRU)</p>
<p>11.3 Spatial deep learning (CNN)</p>
<p>Chapter 12 Hydrometeorological Applications of deep learning</p>
<p>12.1 Stochastic simulation with LSTM</p>
<p>12.2 Forecasting daily temperature with LSTM</p>
<p>Chapter 13 Environmental Applications of deep learning</p>
<p>13.1 Remote sensing of water quality using CNN</p>
<p> </p>
<p>1.1 What is deep learning?</p>
<p>1.2 Pros and cons of deep learning</p>
<p>1.3 Recent applications of deep learning in hydrometeorological and environmental studies</p>
<p>1.4 Organization of chapters</p>
<p>1.5 Summary and conclusion</p>
<p>Chapter 2 Mathematical Background</p>
<p>2.1 Linear regression model</p>
<p>2.2 Time series model</p>
<p>2.3 Probability distributions</p>
<p>Chapter 3 Data Preprocessing</p>
<p>3.1 Normalization</p>
<p>3.2 Data splitting for training and testing</p>
<p>Chapter 4 Neural Network</p>
<p>4.1 Terminology in neural network</p>
<p>4.2 Artificial neural network</p>
<p>Chapter 5 . Training a Neural Network</p>
<p>5.1 Initialization</p>
<p>5.2 Gradient descent</p>
<p>5.3 Backpropagation</p>
<p>Chapter 6 . Updating Weights</p>
<p>6.1 Momentum</p>
<p>6.2 Adagrad</p>
<p>6.3 RMSprop</p>
<p>6.4 Adam</p>
<p>6.5 Nadam</p>
<p>6.6 Python coding of updating weights</p>
<p>Chapter 7 . Improving model performance</p>
<p>7.1 Batching and minibatch</p>
<p>7.2 Validation</p>
<p>7.3 Regularization</p>
<p>Chapter 8 Advanced Neural Network Algorithms</p>
<p>8.1 Extreme Learning Machine (ELM)</p>
<p>8.2 Autoencoding</p>
<p>Chapter 9 Deep learning for time series</p>
<p>9.1 Recurrent neural network</p>
<p>9.2 Long Short-Term Memory (LSTM)</p>
<p>9.3 Gated Recurrent Unit (GRU)</p>
<p>Chapter 10 Deep learning for spatial datasets</p>
<p>10.1 Convolutional Neural Network (CNN)</p>
10.2 Backpropagation of CNN<p></p>
<p>Chapter 11 Tensorflow and Keras Programming for Deep Learning</p>
<p>11.1 Basic Keras modeling</p>
<p>11.2 Temporal deep learning (LSTM and GRU)</p>
<p>11.3 Spatial deep learning (CNN)</p>
<p>Chapter 12 Hydrometeorological Applications of deep learning</p>
<p>12.1 Stochastic simulation with LSTM</p>
<p>12.2 Forecasting daily temperature with LSTM</p>
<p>Chapter 13 Environmental Applications of deep learning</p>
<p>13.1 Remote sensing of water quality using CNN</p>
<p> </p>
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