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.

Table of content:PrefaceChapter 1. Time Series Analysis1.1. State Estimation 1.2. Power Spectrum1.3. Optimal Filtering1.4. State Space Models 1.5. Information Theory1.6. Complex NetworksChapter 2. Dynamical Systems2.1. State-Space Reconstruction2.2. Determinism and Predictability2.3. Embedding Methods 2.4. Lyapunov Exponents2.5. Modelling and Forecasting2.6. Chaos and nonlinear noise reductionChapter 3. Approximation3.1. Trigonometric Approximation3.2. Polynomial Approximation3.3. Spline Approximation3.4. Rational Approximation3.5. Wavelet Approximation3.6. Multivariate Approximation3.7. Dimensionality reduction3.8. Adaptive Basis Selection and Greedy AlgorithmChapter 4. Interpolation4.1. Curve Fitting4.2. Lagrange Interpolation4.3. Hermite Interpolation4.4. Spline Interpolation4.5. Case StudiesChapter 5. Satistical Methods5.1. Linear Regression5.2. Logistic Regression5.3. Multiple Regression5.4. Analysis of Covariance5.5. Cluster Analysis 5.6. Discriminant Analysis.5.7. Principal Component Analysis 5.8. Factor Analysis5.9. SPSS softwareChapter 6. Numerical Methods 6.1. Numerical Integration6.2. Numerical Differentiation6.3. Direct and Iterative Methods6.4. Finite Difference Methods. 6.5. Finite Element Methods.6.6. Finite Volume Methods6.7. Wavelet MethodsChapter 7. Optimization7.1. Steepest Descent and Newton methods7.2. Linear optimization7.3. Lagrange multipliers7.4. Karush-Kuhn-Tucker conditions7.5. Primal-dual interior-point method7.6. The simplex method 7.7. Stochastic optimizationChapter 8. Risk Assessments Chapter 9. Life Cycle Assessments