Description
Book SynopsisMost 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
Table of ContentsTable 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
