Description
Springer Deep Learning for Hydrometeorology and Environmental Science by Taesam Lee, Vijay P. Singh, Kyung Hwa Cho
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). _x000D__x000D_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._x000D__x000D_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._x000D__x000D__x000D__x000D_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._x000D_ Table of contents : - _x000D_
Chapter 1 Introduction_x000D_
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1.1 What is deep learning?_x000D_
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1.2 Pros and cons of deep learning_x000D_
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1.3 Recent applications of deep learning in hydrometeorological and environmental studies_x000D_
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1.4 Organization of chapters_x000D_
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1.5 Summary and conclusion_x000D_
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Chapter 2 Mathematical Background_x000D_
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2.1 Linear regression model_x000D_
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2.2 Time series model_x000D_
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2.3 Probability distributions_x000D_
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Chapter 3 Data Preprocessing_x000D_
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3.1 Normalization_x000D_
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3.2 Data splitting for training and testing_x000D_
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Chapter 4 Neural Network_x000D_
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4.1 Terminology in neural network_x000D_
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4.2 Artificial neural network_x000D_
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Chapter 5 . Training a Neural Network_x000D_
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5.1 Initialization_x000D_
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5.2 Gradient descent_x000D_
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5.3 Backpropagation_x000D_
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Chapter 6 . Updating Weights_x000D_
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6.1 Momentum_x000D_
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6.2 Adagrad_x000D_
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6.3 RMSprop_x000D_
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6.4 Adam_x000D_
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6.5 Nadam_x000D_
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6.6 Python coding of updating weights_x000D_
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Chapter 7 . Improving model performance_x000D_
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7.1 Batching and minibatch_x000D_
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7.2 Validation_x000D_
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7.3 Regularization_x000D_
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Chapter 8 Advanced Neural Network Algorithms_x000D_
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8.1 Extreme Learning Machine (ELM)_x000D_
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8.2 Autoencoding_x000D_
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Chapter 9 Deep learning for time series_x000D_
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9.1 Recurrent neural network_x000D_
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9.2 Long Short-Term Memory (LSTM)_x000D_
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9.3 Gated Recurrent Unit (GRU)_x000D_
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Chapter 10 Deep learning for spatial datasets_x000D_
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10.1 Convolutional Neural Network (CNN)_x000D_
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10.2 Backpropagation of CNN_x000D_
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Chapter 11 Tensorflow and Keras Programming for Deep Learning_x000D_
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11.1 Basic Keras modeling_x000D_
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11.2 Temporal deep learning (LSTM and GRU)_x000D_
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11.3 Spatial deep learning (CNN)_x000D_
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Chapter 12 Hydrometeorological Applications of deep learning_x000D_
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12.1 Stochastic simulation with LSTM_x000D_
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12.2 Forecasting daily temperature with LSTM_x000D_
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Chapter 13 Environmental Applications of deep learning_x000D_
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13.1 Remote sensing of water quality using CNN_x000D_