Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (293 pages)

Ausgabe: 1st ed.

ISBN: 981-9994-08-X

Serie: Innovations in Sustainable Technologies and Computing Series

Anmerkung: Intro -- Foreword by Dr. Martyn Clark -- Foreword by Dr. Rangan Banerjee -- Preface -- Acknowledgements -- Contents -- About the Authors -- Part I Practical Python for a Water and Environment Professional -- 1 Data Analysis in the Water and Environment -- 1.1 Introduction -- 1.2 Types of Data -- 2 Python Environment and Basics -- 2.1 Integrated Development Environment (IDE) -- 2.2 Why Virtual Environments? -- 2.3 The Anaconda Package Manager -- 2.4 The Jupyter Notebook -- 2.5 Installing External Packages -- 3 Python Essentials -- 3.1 Getting Started with Python -- 3.2 Setting Up Python Environment -- 3.3 My First Python Script -- 3.4 Python Fundamentals -- 3.4.1 Basic Syntax -- 3.4.2 Functions -- 3.4.3 List and Tuples -- 3.4.4 Dictionaries and Dataframes -- 3.4.5 Loops -- 3.4.6 Conditional Statements in Python -- 3.4.7 File Operations in Python -- 4 Exploratory Analysis of Hydrological Data -- 4.1 Examining a Dataset -- 4.1.1 Types of Data -- 4.1.2 Basic Data Characteristics -- 4.1.3 Common Variable Types -- 4.2 Summarizing a Dataset -- 4.2.1 Theoretical Probability Distributions and Applications -- 4.2.2 Summarizing Numerical Data -- 4.2.3 Gaussianity in Numerical Data -- 4.2.4 Limitations of Summary Statistics -- 4.2.5 Fitting a Distribution -- 4.2.6 Inliers and Outliers in Hydrologic Data -- 4.2.7 Missing Data -- 4.2.8 Q-Q Plots -- 5 Graphical Hydrological Data Analysis -- 5.1 For a Single Dataset -- 5.1.1 Histograms -- 5.1.2 Boxplots -- 5.1.3 Quantile Plots -- 5.2 For Multivariate Data -- 5.2.1 Scatter Matrix Plot -- 5.2.2 Parallel Coordinate Plot -- 5.3 Publication-Ready Graphics -- 5.4 Misleading Graphics -- Part II Statistical Modeling in Hydrology -- 6 Curve Fitting and Regression Analysis -- 6.1 Simple Linear Regression of Flow -- 6.2 Multiple Linear Regression of Flow -- 6.3 Nonlinear Regression of Flow. , 7 Hydrological Time Series Analysis -- 7.1 Stationarity, Trend, and Periodicity -- 7.2 Common Forecasting Methods -- 7.2.1 Autoregression (AR) -- 7.2.2 Autoregressive Moving Average (ARMA) -- 7.2.3 Autoregressive Integrated Moving Average (ARIMA) -- 7.2.4 Simple Exponential Smoothing (SES) -- 8 Common Hypothesis Testing -- 8.1 One-Way Analysis of Variance -- 8.2 Two-Way Analysis of Variance -- 8.3 t-Test -- 8.4 F-Test -- 8.5 The Kolmogorov-Smirnov Test -- 8.6 Mann-Whitney Test -- 9 Uncertainty Estimation -- 9.1 Interval Estimates -- 9.1.1 Non-parametric Interval Estimate -- 9.2 Confidence Intervals -- 9.2.1 For Median -- 9.2.2 For Mean -- 9.2.3 For Quantiles -- 9.3 Prediction Intervals -- 9.3.1 Non-parametric Prediction Interval -- 9.3.2 One-Sided Non-parametric Prediction Interval -- 9.3.3 Two-Sided Parametric Prediction Interval -- 9.3.4 Asymmetric Prediction Interval -- 9.4 Quantile Regression -- 9.5 Maximum Likelihood Estimation (MLE) -- 9.6 Monte Carlo Uncertainty Propagation -- Part III Surface and Subsurface Water -- 10 Introduction -- 10.1 Numerical Modeling Using Finite Elements -- 10.2 Weak Form of the Steady State Darcy Flow Equation -- 10.3 Integration of Transient PDEs -- 11 Surface Flow Models -- 11.1 Rectangular Channel -- 11.1.1 Python Code -- 11.2 Triangular Channel -- 11.2.1 Python Code -- 11.3 Circular Channel -- 11.3.1 Python Code -- 11.3.2 Discussion of Results -- 11.4 2D Shallow Water Equations -- 11.4.1 Governing Equations -- 11.4.2 Python Code -- 11.4.3 Discussion of the Results -- 12 Subsurface Flow Models -- 12.1 Seepage Flow Model -- 12.1.1 Variational Formulation -- 12.1.2 GMSH Code to Generate the Computational Domain -- 12.1.3 Python Code -- 12.1.4 Discussion of the Results -- 12.2 Groundwater Flow Model -- 12.2.1 Variational Formulation -- 12.2.2 GMSH Code to Create the Computational Domain -- 12.2.3 Python Code. , 12.2.4 Discussion of the Results -- Part IV Environmental Applications -- 13 Transport Phenomena -- 13.1 Contaminant Transport Processes -- 13.2 1D Diffusion Equation -- 13.3 1D Diffusion-Reaction Equation -- 13.4 1D Advection-Diffusion Equation -- 13.5 A 2D Simulation Using Navier-Stokes Equations -- 13.5.1 Construction of the Computational Domain -- 13.5.2 GMSH Code to Generate the Computational Domain -- 13.5.3 Variational Formulation -- 13.5.4 Python Code -- 13.5.5 Post-processing of the Results -- 13.5.6 Discussion of the Results -- 14 Contaminant Transport Models -- 14.1 A 2D Diffusion Reaction Model -- 14.1.1 Variational Formulation -- 14.1.2 Python Code -- 14.1.3 Post-processing -- 14.1.4 Discussion of the Results -- 14.2 A 2D Diffusion Advection Model -- 14.2.1 Variational Formulation -- 14.2.2 Python Code -- 14.2.3 Post-processing -- 14.2.4 Discussion of the Results -- 14.3 Generalized 2D Advection, Diffusion, Reaction Model -- 14.3.1 Variational Formulation -- 14.3.2 Python Code -- 14.3.3 Post-processing -- 14.3.4 Discussion of the Results -- 15 Conclusion.

Weitere Ausg.: ISBN 981-9994-07-1

Sprache: Englisch