Environmental Data Analysis :Methods and Applications

Publication subTitle :Methods and Applications

Author: Zhang Zhihua  

Publisher: De Gruyter‎

Publication year: 2017

E-ISBN: 9783110424904

P-ISBN(Paperback): 9783110430011

Subject: X11 environmental mathematics

Keyword: 自然地理学,应用数学,数学

Language: ENG

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Description

Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields.

Contents:
Preface
Time series analysis
Chaos and dynamical systems
Approximation
Interpolation
Statistical methods
Numerical methods
Optimization
Data envelopment analysis
Risk assessments
Life cycle assessments
Index

Chapter

1.5 Prediction and modeling of ARMA processes

1.6 Multivariate ARMA processes

1.7 State-space models

2 Chaos and dynamical systems

2.1 Dynamical systems

2.2 Henon and logistic maps

2.3 Lyapunov exponents

2.4 Fractal dimension

2.5 Prediction

2.6 Delay embedding vectors

2.7 Singular spectrum analysis

2.8 Recurrence networks

3 Approximation

3.1 Trigonometric approximation

3.2 Multivariate approximation and dimensionality reduction

3.3 Polynomial approximation

3.4 Spline approximation and rational approximation

3.5 Wavelet approximation

3.6 Greedy algorithms

4 Interpolation

4.1 Curve fitting

4.2 Lagrange interpolation

4.3 Hermite interpolation

4.4 Spline interpolation

4.5 Trigonometric interpolation and fast Fourier transform

4.6 Bivariate interpolation

5 Statistical methods

5.1 Linear regression

5.2 Multiple regression

5.3 Case study: Tree-ring-based climate reconstructions

5.4 Covariance analysis

5.5 Discriminant analysis

5.6 Cluster analysis

5.7 Principal component analysis

5.8 Canonical correlation analysis

5.9 Factor analysis

6 Numerical methods

6.1 Numerical integration

6.2 Numerical differentiation

6.3 Iterative methods

6.4 Difference methods

6.5 Finite element methods

6.6 Wavelet methods

7 Optimization

7.1 Newton’s method and steepest descent method

7.2 The variational method

7.3 The simplex method

7.4 Fermat rules

7.5 Karush–Kuhn–Tucker optimality conditions

7.6 Primal and dual pairs of linear optimization

7.7 Case studies

8 Data envelopment analysis

8.1 Charnes–Cooper–Rhodes DEA models

8.2 Banker–Charnes–Cooper DEA models

8.3 One-stage and two-stage methods

8.4 Advanced DEA models

8.5 Software and case studies

9 Risk assessments

9.1 Decision rules under uncertainty

9.2 Decision trees

9.3 Fractile and triangular methods

9.4 The ε-constraint method

9.5 The uncertainty sensitivity index method

9.6 The partitioned multiobjective risk method

9.7 The multiobjective multistage impact analysis method

9.8 Multiobjective risk impact analysis method

9.9 The Leslie model

9.10 Leontief’s and inoperability input-output models

10 Life cycle assessments

10.1 Classic life cycle assessment

10.2 Exergetic life cycle assessment

10.3 Ecologically-based life cycle assessment

10.4 Case studies

Index

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