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 Ba
About the Author: Professor Taesam Lee, Ph.D. is a full professor in the Department of Civil Engineering at Gyeongsang National University in Jinju, South Korea. He got his Ph.D. degree from Colorado State University with stochastic simulation of streamflow. He specializes in surface-water hydrology, meteorology, machine learning algorithms, and climatic changes in hydrological extremes publishing around 50 technical papers and a statistical downscaling book. He is a member of American Society of Civil Engineers (ASCE) and American Geophysical Union (AGU) and the associate editor of Journal of Hydrologic Engineering in ASCE.
Professor V.P. Singh is a University Distinguished Professor, a Regents Professor, and Caroline and William N. Lehrer Distinguished Chair in Water Engineering at Texas A&M University. He received his B.S., M.S., Ph.D. and D.Sc. degrees in engineering. He is a registered professional engineer, a registered professional hydrologist, and an Honorary diplomate of ASCE-AAWRE. He has published more than 1270 journal articles; 30 textbooks; 70 edited reference books; 105 book chapters; and 315 conference papers in the area of hydrology and water resources. He has received more than 90 national and international awards, including three honorary doctorates. He is a member of 11 international science/engineering academies. He has served as President of the American Institute of Hydrology (AIH), Chair of Watershed Council of American Society of Civil Engineers, and is currently President-Elect of American Academy of Water Resources Engineers. He has served/serves as editor-in-chief of three journals and two book series and serves on editorial boards of more than 25 journals and three book series.
Professor Kyung Hwa Cho, Ph.D. is an associate professor in the urban and environmental at Ulsan National Institute of Science and Technology, South Korea. He obtained his B.S in chemical engineering and M.S. and Ph.D. in Environmental Engineering. He has published more than 110 journal articles in water and environmental journals such as Water Research, Remote Sensing of Environment. His expertise lies in modeling water quality, deep learning application for water quality prediction, and using hyperspectral images for water quality monitoring.
