Environmental Data Analysis: Methods and Applications
With the dramatic development of air-space-ground-sea environmental monitoring networks and large-scale high-resolution Earth simulators, Environmental science is facing opportunities and challenges of big data. Environmental Data Analysis focuses on state-of-the-art models and methods for big environmental data and demonstrates their applications through various case studies in the real world. It covers the comprehensive range of topics in data analysis in space, time and spectral domains, including linear and nonlinear environmental systems, feature extraction models, data envelopment analysis, risk assessments, and life cycle assessments. The 2nd Edition adds emerging network models, including neural networks, complex networks, downscaling analysis and streaming data on network.

This book is a concise and self-contained work with enormous amount of information. It is a must-read for environmental scientists who struggle to conduct big data mining and data scientists who try to find the way into environmental science.

1124197978
Environmental Data Analysis: Methods and Applications
With the dramatic development of air-space-ground-sea environmental monitoring networks and large-scale high-resolution Earth simulators, Environmental science is facing opportunities and challenges of big data. Environmental Data Analysis focuses on state-of-the-art models and methods for big environmental data and demonstrates their applications through various case studies in the real world. It covers the comprehensive range of topics in data analysis in space, time and spectral domains, including linear and nonlinear environmental systems, feature extraction models, data envelopment analysis, risk assessments, and life cycle assessments. The 2nd Edition adds emerging network models, including neural networks, complex networks, downscaling analysis and streaming data on network.

This book is a concise and self-contained work with enormous amount of information. It is a must-read for environmental scientists who struggle to conduct big data mining and data scientists who try to find the way into environmental science.

185.99 In Stock
Environmental Data Analysis: Methods and Applications

Environmental Data Analysis: Methods and Applications

by Zhihua Zhang
Environmental Data Analysis: Methods and Applications

Environmental Data Analysis: Methods and Applications

by Zhihua Zhang

Hardcover(2nd Edition)

$185.99 
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Overview

With the dramatic development of air-space-ground-sea environmental monitoring networks and large-scale high-resolution Earth simulators, Environmental science is facing opportunities and challenges of big data. Environmental Data Analysis focuses on state-of-the-art models and methods for big environmental data and demonstrates their applications through various case studies in the real world. It covers the comprehensive range of topics in data analysis in space, time and spectral domains, including linear and nonlinear environmental systems, feature extraction models, data envelopment analysis, risk assessments, and life cycle assessments. The 2nd Edition adds emerging network models, including neural networks, complex networks, downscaling analysis and streaming data on network.

This book is a concise and self-contained work with enormous amount of information. It is a must-read for environmental scientists who struggle to conduct big data mining and data scientists who try to find the way into environmental science.


Product Details

ISBN-13: 9783111012544
Publisher: De Gruyter
Publication date: 02/20/2023
Edition description: 2nd Edition
Pages: 400
Product dimensions: 6.69(w) x 9.45(h) x 0.03(d)
Age Range: 18 Years

About the Author

Zhihua Zhang is a Taishan Distinguished Professor at Shandong University, China. His research interests are Big Data Mining, Climate Change Mechanisms, Environmental Evolution and Sustainability. He has published 6 first-authored books in Elsevier/Springer/DeGruyter and published more than 60 first-authored articles, some of which were reported by New Scientist (UK), China Science Daily, and China Social Science Daily. Prof. Zhang is serving as the Editor-in-Chief of Int J Big Data Mining for Global Warming (World Scientific), Topical Chief Editor of Arab J Geosci (Springer), Associate Editor of Environ Dev Sustain (Springer), Associate Editor of EURASIP J Adv Signal Process (Springer), Associate Editor of Int J Climate Change Strat & Manag (Emerald), etc.

Table of Contents

Table of content:
Preface
Chapter 1. Time Series Analysis
1.1. State Estimation
1.2. Power Spectrum
1.3. Optimal Filtering
1.4. State Space Models
1.5. Information Theory
1.6. Complex Networks
Chapter 2. Dynamical Systems
2.1. State-Space Reconstruction
2.2. Determinism and Predictability
2.3. Embedding Methods
2.4. Lyapunov Exponents
2.5. Modelling and Forecasting
2.6. Chaos and nonlinear noise reduction
Chapter 3. Approximation
3.1. Trigonometric Approximation
3.2. Polynomial Approximation
3.3. Spline Approximation
3.4. Rational Approximation
3.5. Wavelet Approximation
3.6. Multivariate Approximation
3.7. Dimensionality reduction
3.8. Adaptive Basis Selection and Greedy Algorithm
Chapter 4. Interpolation
4.1. Curve Fitting
4.2. Lagrange Interpolation
4.3. Hermite Interpolation
4.4. Spline Interpolation
4.5. Case Studies
Chapter 5. Satistical Methods
5.1. Linear Regression
5.2. Logistic Regression
5.3. Multiple Regression
5.4. Analysis of Covariance
5.5. Cluster Analysis
5.6. Discriminant Analysis.
5.7. Principal Component Analysis
5.8. Factor Analysis
5.9. SPSS software
Chapter 6. Numerical Methods
6.1. Numerical Integration
6.2. Numerical Differentiation
6.3. Direct and Iterative Methods
6.4. Finite Difference Methods.
6.5. Finite Element Methods.
6.6. Finite Volume Methods
6.7. Wavelet Methods
Chapter 7. Optimization
7.1. Steepest Descent and Newton methods
7.2. Linear optimization
7.3. Lagrange multipliers
7.4. Karush-Kuhn-Tucker conditions
7.5. Primal-dual interior-point method
7.6. The simplex method
7.7. Stochastic optimization
Chapter 8. Risk Assessments
Chapter 9. Life Cycle Assessments

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