Deep learning for hydrometeorology and environmental science
Author(s)
Bibliographic Information
Deep learning for hydrometeorology and environmental science
（Water science and technology library, v. 99）
Springer, c2021
Available at / 2 libraries
-
No Libraries matched.
- Remove all filters.
Description and Table of Contents
Description
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.
Table of Contents
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
by "Nielsen BookData"