| Description |
1 online resource (346 pages) |
| Contents |
Front Cover; Big Data Mining for Climate Change; Copyright; Contents; Preface; 1 Big climate data; 1.1 Big data sources; 1.1.1 Earth observation big data; 1.1.2 Climate simulation big data; 1.2 Statistical and dynamical downscaling; 1.3 Data assimilation; 1.3.1 Cressman analysis; 1.3.2 Optimal interpolation analysis; 1.3.3 Three-dimensional variational analysis; 1.3.4 Four-dimensional variational analysis; 1.4 Cloud platforms; 1.4.1 Cloud storage; 1.4.2 Cloud computing; Further reading; 2 Feature extraction of big climate data; 2.1 Clustering; 2.1.1 K-means clustering |
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2.1.2 Hierarchical clustering2.2 Hidden Markov model; 2.3 Expectation maximization; 2.4 Decision trees and random forests; 2.5 Ridge and lasso regressions; 2.6 Linear and quadratic discriminant analysis; 2.6.1 Bayes classi er; 2.6.2 Linear discriminant analysis; 2.6.3 Quadratic discriminant analysis; 2.7 Support vector machines; 2.7.1 Maximal margin classi er; 2.7.2 Support vector classi ers; 2.7.3 Support vector machines; 2.8 Rainfall estimation; 2.9 Flood susceptibility; 2.10 Crop recognition; Further reading; 3 Deep learning for climate patterns; 3.1 Structure of neural networks |
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3.2 Back propagation neural networks3.2.1 Activation functions; 3.2.2 Back propagation algorithms; 3.3 Feedforward multilayer perceptrons; 3.4 Convolutional neural networks; 3.5 Recurrent neural networks; 3.5.1 Input-output recurrent model; 3.5.2 State-space model; 3.5.3 Recurrent multilayer perceptrons; 3.5.4 Second-order network; 3.6 Long short-term memory neural networks; 3.7 Deep networks; 3.7.1 Deep learning; 3.7.2 Boltzmann machine; 3.7.3 Directed logistic belief networks; 3.7.4 Deep belief nets; 3.8 Reinforcement learning; 3.9 Dendroclimatic reconstructions |
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3.10 Downscaling climate variability3.11 Rainfall-runoff modeling; Further reading; 4 Climate networks; 4.1 Understanding climate systems as networks; 4.2 Degree and path; 4.3 Matrix representation of networks; 4.4 Clustering and betweenness; 4.5 Cut sets; 4.6 Trees and planar networks; 4.7 Bipartite networks; 4.8 Centrality; 4.8.1 Degree centrality; 4.8.2 Closeness centrality; 4.8.3 Betweenness centrality; 4.9 Similarity; 4.9.1 Cosine similarity; 4.9.2 Pearson similarity; 4.10 Directed networks; 4.11 Acyclic directed networks; 4.12 Weighted networks; 4.12.1 Vertex strength |
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4.12.2 Weight-degree/weight-weight correlation4.12.3 Weighted clustering; 4.12.4 Shortest path; 4.13 Random walks; 4.14 El Niño southern oscillation; 4.15 North Atlantic oscillation; Further reading; 5 Random climate networks and entropy; 5.1 Regular networks; 5.1.1 Fully connected networks; 5.1.2 Regular ring-shaped networks; 5.1.3 Star-shaped networks; 5.2 Random networks; 5.2.1 Giant component; 5.2.2 Small component; 5.3 Con guration networks; 5.3.1 Edge probability and common neighbor; 5.3.2 Degree distribution; 5.3.3 Giant components; 5.3.4 Small components; 5.3.5 Directed random network |
| Summary |
Delivering a rich understanding of climate-related big data techniques, this comprehensive book highlights how to navigate huge amount of climate data and resources available using big data applications. -- Edited summary from book |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed December 4, 2019) |
| Subject |
Climatology -- Statistical methods
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Data mining.
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Data Mining
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Climatology -- Statistical methods
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Data mining
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| Genre/Form |
Electronic books
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| Form |
Electronic book
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| Author |
Li, Jianping, author
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| ISBN |
9780128187043 |
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0128187042 |
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