Note: 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 -- 2.1.2 Hierarchical clustering -- 2.2 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 -- 3.2 Back propagation neural networks -- 3.2.1 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. , 3.10 Downscaling climate variability -- 3.11 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 -- 4.12.2 Weight-degree/weight-weight correlation -- 4.12.3 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 -- 5.4 Small-world networks -- 5.4.1 Main models -- 5.4.2 Degree distribution -- 5.4.3 Clustering -- 5.4.4 Mean distance -- 5.5 Power-law degree distribution -- 5.5.1 Price's models -- 5.5.2 Barabasi-Albert models -- 5.6 Dynamics of random networks -- 5.7 Entropy and joint entropy -- 5.8 Conditional entropy and mutual information -- 5.9 Entropy rate -- 5.10 Entropy-based climate network -- 5.11 Entropy-based decision tree -- Further reading -- 6 Spectra of climate networks -- 6.1 Understanding atmospheric motions via network spectra -- 6.2 Adjacency spectra -- 6.2.1 Maximum degree -- 6.2.2 Diameter. , 6.2.3 Paths of length k -- 6.3 Laplacian spectra -- 6.3.1 Maximum degree -- 6.3.2 Connectivity -- 6.3.3 Spanning tree -- 6.3.4 Degree sequence -- 6.3.5 Diameter -- 6.4 Spectrum centrality -- 6.4.1 Eigenvector centrality -- 6.4.2 Katz centrality -- 6.4.3 Pagerank centrality -- 6.4.4 Authority and hub centralities -- 6.5 Network eigenmodes -- 6.6 Spectra of complete networks -- 6.7 Spectra of small-world networks -- 6.8 Spectra of circuit and wheel network -- 6.9 Spectral density -- 6.10 Spectrum-based partition of networks -- Further reading -- 7 Monte Carlo simulation of climate systems -- 7.1 Random sampling -- 7.1.1 Uniform distribution -- 7.1.2 Nonuniform distribution -- 7.1.3 Normal distribution -- 7.2 Variance reduction technique -- 7.2.1 Control variable method -- 7.2.2 Control vector method -- 7.3 Strati ed sampling -- 7.4 Sample paths for Brownian motion -- 7.4.1 Cholesky and Karhounen-Loève expansions -- 7.4.2 Brownian bridge -- 7.5 Quasi-Monte Carlo method -- 7.5.1 Discrepancy -- 7.5.2 Koksma-Hlawka inequality -- 7.5.3 Van der Corput sequence -- 7.5.4 Halton sequence -- 7.5.5 Faure sequence -- 7.6 Markov chain Monte Carlo -- 7.7 Gibbs sampling -- Further reading -- 8 Sparse representation of big climate data -- 8.1 Global positioning -- 8.1.1 Multidimensional scaling -- 8.1.2 Local rigid embedding -- 8.2 Embedding rules -- 8.2.1 Attractors and fractal dimension -- 8.2.2 Delay embedding -- 8.2.3 Multichannel singular spectrum analysis -- 8.2.4 Recurrence networks -- 8.3 Sparse recovery -- 8.3.1 Sparse interpolation -- 8.3.2 Sparse approximation -- 8.3.3 Greedy algorithms -- 8.4 Sparse representation of climate modeling big data -- 8.5 Compressive sampling of remote sensing big data -- 8.5.1 s-Sparse approximation -- 8.5.2 Minimal samples -- 8.5.3 Orthogonal matching pursuit -- 8.5.4 Compressive sampling matching pursuit. , 8.5.5 Iterative hard thresholding -- 8.6 Optimality -- 8.6.1 Optimization algorithm for compressive sampling -- 8.6.2 Chambolle and Pock's primal-dual algorithm -- Further reading -- 9 Big-data-driven carbon emissions reduction -- 9.1 Precision agriculture -- 9.2 Oil exploitation -- 9.3 Smart buildings -- 9.4 Smart grids -- 9.5 Smart cities -- Further reading -- 10 Big-data-driven low-carbon management -- 10.1 Large-scale data envelopment analysis -- 10.2 Natural resource management -- 10.3 Roadway network management -- 10.4 Supply chain management -- 10.5 Smart energy management -- Further reading -- 11 Big-data-driven Arctic maritime transportation -- 11.1 Trans-Arctic routes -- 11.2 Sea-ice remote-sensing big data -- 11.2.1 Arctic sea-ice concentration -- 11.2.2 Melt ponds -- 11.2.3 Arctic sea-ice extent -- 11.2.4 Arctic sea-ice thickness -- 11.2.5 Arctic sea-ice motion -- 11.2.6 Comprehensive integrated observation system -- 11.3 Sea-ice modeling big data -- 11.4 Arctic transport accessibility model -- 11.5 Economic and risk assessments of Arctic routes -- 11.6 Big-data-driven dynamic optimal trans-Arctic route system -- 11.7 Future prospects -- Further reading -- Index -- Back Cover.