Deep Learning for Hydrometeorology and Environmental Science

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