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  • 1
    Online Resource
    Online Resource
    Deep learning for hydrometeorology and environmental science (2021)
    Cham, Switzerland : Springer
    Details
    UID:
    b3kat_BV047135703
    Format: 1 Online-Ressource (xiv, 204 Seiten) , Illustrationen
    ISBN: 9783030647773
    Series Statement: Water science and technology library volume 99
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-64776-6
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-64778-0
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-64779-7
    Language: English
    Keywords: Hydrologie ; Einzugsgebiet ; Modellierung ; Wasserwirtschaft ; Governance
    DOI: 10.1007/978-3-030-64777-3
    URL: Volltext  (URL des Erstveröffentlichers)
    Author information: Singh, Vijay P. 1946-
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  • 2
    Online Resource
    Online Resource
    Deep Learning for Hydrometeorology and Environmental Science (2021)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    UID:
    gbv_1746360992
    Format: 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
    Content: 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.
    Content: 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.
    Additional Edition: ISBN 9783030647766
    Additional Edition: ISBN 9783030647780
    Additional Edition: ISBN 9783030647797
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030647766
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030647780
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030647797
    Language: English
    Keywords: Hydrometeorologie ; Hydrologie ; Maschinelles Lernen ; Angewandte Mathematik ; Wasserhaushalt ; Prognose ; Neuronales Netz ; Modellierung ; Mathematisches Modell
    DOI: 10.1007/978-3-030-64777-3
    URL: lizenzpflichtig
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    UB Potsdam
  • 3
    Online Resource
    Online Resource
    Deep learning for hydrometeorology and environmental science (2021)
    Cham : Springer
    Show associated volumes
    Details
    UID:
    kobvindex_GFZBV047135703
    Format: 1 Online-Ressource (xiv, 204 Seiten) , Illustrationen, Diagramme, Karten
    ISBN: 9783030647773 , 978-3-030-64777-3
    ISSN: 0921-092X , 1872-4663
    Series Statement: Water science and technology library volume 99
    Content: 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.
    Note: Contents 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
    In: Water science and technology library, volume 99
    Language: English
    Keywords: Electronic books
    DOI: 10.1007/978-3-030-64777-3
    URL: Ebook (access only within the AWI network)
    Author information: Singh, Vijay P. 1946-
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    Wissenschaftspark Albert Einstein
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