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Deep Learning for Hydrometeorology and Environmental Science. (2021)

Lee, Taesam.

Cham :Springer International Publishing AG,

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Keywords: Machine learning. ; Electronic books.

Type of Medium: Online Resource

Pages: 1 online resource (215 pages)

Edition: 1st ed.

ISBN: 9783030647773

Series Statement: Water Science and Technology Library ; v.99

URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=6465115

DDC: 006.31

Language: English

Note: Intro -- Preface -- Contents -- About the Authors -- 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 -- References -- 2 Mathematical Background -- 2.1 Linear Regression Model -- 2.1.1 Simple Linear Regression -- 2.1.2 Multiple Linear Regression -- 2.2 Time Series Model -- 2.2.1 Autoregressive Model (AR) -- 2.3 Probability Distributions -- 2.3.1 Normal Distributions -- 2.3.2 Gamma Distribution -- 2.4 Exercises -- References -- 3 Data Preprocessing -- 3.1 Normalization -- 3.2 Data Splitting for Training and Testing -- 3.3 Exercises -- 4 Neural Network -- 4.1 Terminology in Neural Network -- 4.1.1 Components of Neural Network -- 4.1.2 Activation Functions -- 4.1.3 Error and Loss Function -- 4.1.4 Softmax and One-Hot Encoding -- 4.2 Artificial Neural Network -- 4.2.1 Simplest Network -- 4.2.2 Feedforward and Backward Propagation -- 4.2.3 Network with Multiple Input and Output Variables -- 4.2.4 Python Coding of the Simple Network -- 4.3 Exercises -- 5 Training a Neural Network -- 5.1 Initialization -- 5.2 Gradient Descent -- 5.3 Backpropagation -- 5.3.1 Simple Network -- 5.3.2 Full Neural Network -- 5.3.3 Python Coding of Network -- 5.4 Exercises -- Reference -- 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 -- 6.7 Exercises -- References -- 7 Improving Model Performance -- 7.1 Batching and Minibatch -- 7.2 Validation -- 7.2.1 Python Coding of K-Fold Cross-Validation -- 7.3 Regularization -- 7.3.1 L-Norm Regularization -- 7.3.2 Dropout -- 7.3.3 Python Coding of Regularization -- 7.4 Exercises -- Reference -- 8 Advanced Neural Network Algorithms -- 8.1 Extreme Learning Machine (ELM). , 8.1.1 Basic ELM -- 8.1.2 Generalized ELM -- 8.1.3 Python Coding -- 8.2 Autoencoder -- 8.2.1 Vanilla Autoencoder -- 8.2.2 Regularized Autoencoder -- 8.2.3 Python Coding of Regularized AE -- 8.3 Exercises -- Reference -- 9 Deep Learning for Time Series -- 9.1 Recurrent Neural Network -- 9.1.1 Backpropagation -- 9.1.2 Backpropagation Through Time (BPTT) -- 9.2 Long Short-Term Memory (LSTM) -- 9.2.1 Basics of LSTM -- 9.2.2 Example of LSTM -- 9.2.3 Backpropagation of a Simple LSTM -- 9.2.4 Backpropagation Through Time (BPTT) -- 9.3 Gated Recurrent Unit (GRU) -- 9.3.1 Basics of GRU -- 9.3.2 Example of GRU -- 9.3.3 Backpropagation of a Simple GRU Model -- 9.4 Exercises -- References -- 10 Deep Learning for Spatial Datasets -- 10.1 Convolutional Neural Network (CNN) -- 10.1.1 Definition of Convolution -- 10.1.2 Elements of CNN -- 10.2 Backpropagation of CNN -- 10.3 Exercises -- 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) -- 11.4 Exercises -- References -- 12 Hydrometeorological Applications of Deep Learning -- 12.1 Stochastic Simulation with LSTM -- 12.1.1 Mathematical Description for Stochastic Simulation with LSTM -- 12.1.2 Colorado Monthly Streamflow -- 12.1.3 Results of Colorado River -- 12.1.4 Python Coding -- 12.1.5 Matlab Coding -- 12.2 Forecasting Daily Temperature with LSTM -- 12.2.1 Preparing the Data -- 12.2.2 Methodology -- 12.2.3 Results -- 12.2.4 Python Coding -- 12.3 Exercises -- References -- 13 Environmental Applications of Deep Learning -- 13.1 Remote Sensing of Water Quality Using CNN -- 13.1.1 Introduction -- 13.1.2 Study Area and Monitoring -- 13.1.3 Field Data Collection -- 13.1.4 Point-Centered Regression CNN (PRCNN) -- 13.1.5 Results and Discussion -- 13.1.6 Conclusion -- 13.1.7 Python Coding -- References.

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Online Resource

Deep Learning for Hydrometeorology and Environmental Science (2021)

Lee, Taesam [VerfasserIn]

Cham : Springer International Publishing | Cham : Imprint: Springer

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Keywords: Water. ; Artificial intelligence. ; Environmental monitoring. ; Environment. ; Neural networks (Computer science) . ; Environmental geography. ; Hydrometeorologie ; Hydrologie ; Maschinelles Lernen ; Angewandte Mathematik ; Wasserhaushalt ; Prognose ; Neuronales Netz ; Modellierung ; Mathematisches Modell

Description / Table of Contents: Introduction -- Mathematical Background -- Data Preprocessing -- Neural Network -- Training a Neural Network -- Updating Weights -- Improving model performance -- Advanced Neural Network Algorithms -- Deep learning for time series -- Deep learning for spatial datasets -- Tensorflow and Keras Programming for Deep Learning -- Hydrometeorological Applications of deep learning -- Environmental Applications of deep learning.

Type of Medium: Online Resource

Pages: 1 Online-Ressource(XIV, 204 p. 189 illus., 133 illus. in color.)

Edition: 1st ed. 2021.

ISBN: 9783030647773

Series Statement: Water Science and Technology Library 99

URL: https://doi.org/10.1007/978-3-030-64777-3

DOI: 10.1007/978-3-030-64777-3

Language: English

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