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Deep Learning for Hydrometeorology and Environmental Science. (2021)

Lee, Taesam.

Cham :Springer International Publishing AG,

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Schlagwort(e): Machine learning. ; Electronic books.

Materialart: Online-Ressource

Seiten: 1 online resource (215 pages)

Ausgabe: 1st ed.

ISBN: 9783030647773

Serie: Water Science and Technology Library ; v.99

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

DDC: 006.31

Sprache: Englisch

Anmerkung: 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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Statistical Downscaling for Hydrological and Environmental Applications. (2018)

Lee, Taesam.

Milton :Taylor & Francis Group,

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Details

Schlagwort(e): Hydrometeorology-Statistical methods. ; Multiscale modeling. ; Electronic books.

Beschreibung / Inhaltsverzeichnis: This book presents statistical downscaling techniques in a practical manner so that readers can easily adopt the techniques for hydrological applications and designs in response to climate change. It also provides numerous examples and background information on reliability of impact assessments of climate change and what the results imply.

Materialart: Online-Ressource

Seiten: 1 online resource (179 pages)

Ausgabe: 1st ed.

ISBN: 9780429861154

URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=5509988

DDC: 551.570727

Sprache: Englisch

Anmerkung: Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- List of Abbreviations -- Authors -- Chapter 1 Introduction -- 1.1 Why Statistical Downscaling? -- 1.2 Climate Models -- 1.3 Statistical Downscaling -- 1.4 Selection of Model Scheme -- 1.5 Structure of Chapters -- 1.6 Summary and Conclusion -- Chapter 2 Statistical Background -- 2.1 Probability and Statistics -- 2.1.1 Probabilistic Theory -- 2.1.1.1 Probability Density Function and Cumulative Distribution Function -- 2.1.1.2 Descriptors of Random Variables -- 2.1.2 Discrete Probability Distributions -- 2.1.2.1 Bernoulli Distribution -- 2.1.2.2 Binomial Distribution -- 2.1.3 Continuous Probability Distributions -- 2.1.3.1 Normal Distribution and Lognormal Distributions -- 2.1.3.2 Exponential and Gamma Distributions -- 2.1.3.3 Generalized Extreme Value and Gumbel Distribution -- 2.1.4 Parameter Estimation for Probability Distributions -- 2.1.4.1 Method of Moments -- 2.1.4.2 Maximum Likelihood Estimation -- 2.1.5 Histogram and Empirical Distribution -- 2.2 Multivariate Random Variables -- 2.2.1 Multivariate Normal Distribution and Its Conditional Distribution -- 2.2.2 Covariance and Correlation -- 2.3 Random Simulation -- 2.3.1 Monte Carlo Simulation and Uniform Random Number -- 2.3.2 Simulation of Probability Distributions -- 2.4 Metaheuristic Algorithm -- 2.4.1 Harmony Search -- 2.5 Summary and Conclusion -- Chapter 3 Data and Format Description -- 3.1 GCM Data -- 3.2 Reanalysis Data -- 3.3 RCM Data -- 3.4 Summary and Conclusion -- Chapter 4 Bias Correction -- 4.1 Why Bias Correction? -- 4.2 Occurrence Adjustment for Precipitation Data -- 4.3 Empirical Adjustment (Delta Method) -- 4.4 Quantile Mapping -- 4.4.1 General Quantile Mapping -- 4.4.2 Nonparametric Quantile Mapping -- 4.4.3 Quantile Delta Mapping -- 4.5 Summary and Comparison. , Chapter 5 Regression Downscalings -- 5.1 Linear Regression Based Downscaling -- 5.1.1 Simple Linear Regression -- 5.1.1.1 Significance Test -- 5.1.2 Multiple Linear Regression -- 5.2 Predictor Selection -- 5.2.1 Stepwise Regression -- 5.2.2 Least Absolute Shrinkage and Selection Operator -- 5.3 Nonlinear Regression Modeling -- 5.3.1 Artificial Neural Network -- 5.4 Summary and Conclusion -- Chapter 6 Weather Generator Downscaling -- 6.1 Mathematical Background -- 6.1.1 Autoregressive Models -- 6.1.2 Multivariate Autoregressive Model -- 6.1.3 Markov Chain -- 6.2 Weather Generator -- 6.2.1 Model Fitting -- 6.2.1.1 Precipitation -- 6.2.1.2 Weather Variables (T[sub(max)], T[sub(min)], SR) -- 6.2.2 Simulation of Weather Variables -- 6.2.2.1 Precipitation -- 6.2.2.2 Weather Variables (T[sub(max)], T[sub(min)], SR) -- 6.2.3 Implementation of Downscaling -- 6.3 Nonparametric Weather Generator -- 6.3.1 Simulation under Current Climate -- 6.3.2 Simulation under Future Climate Scenarios -- 6.4 Summary and Conclusion -- Chapter 7 Weather-Type Downscaling -- 7.1 Classification of Weather Types -- 7.1.1 Empirical Weather Type -- 7.1.2 Objective Weather Type -- 7.2 Generation of Daily Rainfall Sequences -- 7.3 Future Climate with Weather-Type Downscaling -- 7.4 Summary and Conclusion -- Chapter 8 Temporal Downscaling -- 8.1 Background -- 8.1.1 K-Nearest Neighbor Resampling -- 8.2 Daily to Hourly Downscaling -- 8.3 Summary and Conclusion -- Chapter 9 Spatial Downscaling -- 9.1 Mathematical Background -- 9.1.1 Bilinear Interpolation -- 9.1.2 Nearest Neighbor Interpolation -- 9.2 Bias Correction and Spatial Downscaling -- 9.3 Bias Correction and Constructed Analogues -- 9.4 Bias Correction and Stochastic Analogue -- 9.5 Summary and Comparison -- References -- Index.

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Online-Ressource

Deep Learning for Hydrometeorology and Environmental Science (2021)

Lee, Taesam [VerfasserIn]

Cham : Springer International Publishing | Cham : Imprint: Springer

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Schlagwort(e): 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

Beschreibung / Inhaltsverzeichnis: 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.

Materialart: Online-Ressource

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

Ausgabe: 1st ed. 2021.

ISBN: 9783030647773

Serie: 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

Sprache: Englisch

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