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Big data mining for climate change /
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. --
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Elsevier,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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|---|---|---|---|
| 001 | SCIDIR_on1128449504 | ||
| 003 | OCoLC | ||
| 005 | 20231120010425.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 191130s2020 ne o 000 0 eng d | ||
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| 015 | |a GBB9I9306 |2 bnb | ||
| 016 | 7 | |a 019616705 |2 Uk | |
| 019 | |a 1128033675 | ||
| 020 | |a 9780128187043 |q (electronic bk.) | ||
| 020 | |a 0128187042 |q (electronic bk.) | ||
| 020 | |z 9780128187036 | ||
| 020 | |z 0128187034 | ||
| 035 | |a (OCoLC)1128449504 |z (OCoLC)1128033675 | ||
| 050 | 4 | |a QC981 | |
| 082 | 0 | 4 | |a 333.7 |2 23 |
| 100 | 1 | |a Zhang, Zhihua, |e author. | |
| 245 | 1 | 0 | |a Big data mining for climate change / |c Zhihua Zhang, Jianping Li. |
| 260 | |a Amsterdam : |b Elsevier, |c 2020. | ||
| 300 | |a 1 online resource (346 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed December 4, 2019) | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 4.12.2 Weight-degree/weight-weight correlation4.12.3 Weighted clustering; 4.12.4 Shortest path; 4.13 Random walks; 4.14 El Ni�no 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 | |
| 520 | |a 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. -- |c Edited summary from book. | ||
| 650 | 0 | |a Climatology |x Statistical methods. | |
| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Data Mining |0 (DNLM)D057225 | |
| 650 | 6 | |a Exploration de donn�ees (Informatique) |0 (CaQQLa)201-0300292 | |
| 650 | 7 | |a Climatology |x Statistical methods. |2 fast |0 (OCoLC)fst00864308 | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 700 | 1 | |a Li, Jianping, |e author. | |
| 776 | 0 | 8 | |i Print version : |z 9780128187036 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128187036 |z Texto completo |