Environmental Data Analysis With Matlab

Download or read book Environmental Data Analysis with MatLab written by William Menke and published by Elsevier. This book was released on 2011-09-02 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Environmental Data Analysis with MatLab" is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. It is well written and outlines a clear learning path for researchers and students. It uses real world environmental examples and case studies. It has MatLab software for application in a readily-available software environment. Homework problems help user follow up upon case studies with homework that expands them.

Download or read book Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering written by Shahab Araghinejad and published by Springer Science & Business Media. This book was released on 2013-11-26 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.

Download or read book Environmental Modeling written by Ekkehard Holzbecher and published by Springer Science & Business Media. This book was released on 2012-01-10 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book has two aims: to introduce basic concepts of environmental modelling and to facilitate the application of the concepts using modern numerical tools such as MATLAB. It is targeted at all natural scientists dealing with the environment: process and chemical engineers, physicists, chemists, biologists, biochemists, hydrogeologists, geochemists and ecologists. MATLAB was chosen as the major computer tool for modeling, firstly because it is unique in it's capabilities, and secondly because it is available in most academic institutions, in all universities and in the research departments of many companies. In the 2nd edition many chapters will include updated and extended material. In addition the MATLAB command index will be updated and a new chapter on numerical methods will be added. For the second edition of 'Environmental Modeling' the first edition was completely revised. Text and figures were adapted to the recent MATLAB® version. Several chapters were extended. Correspondingly the index of MATLAB commands was extended considerably, which makes the book even more suitable to be used as a reference work by novices. Finally an introduction into numerical methods was added as a new chapter. “/p>

Download or read book Geophysical Data Analysis: Discrete Inverse Theory written by William Menke and published by Academic Press. This book was released on 2012-12-02 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geophysical Data Analysis: Discrete Inverse Theory is an introductory text focusing on discrete inverse theory that is concerned with parameters that either are truly discrete or can be adequately approximated as discrete. Organized into 12 chapters, the book's opening chapters provide a general background of inverse problems and their corresponding solution, as well as some of the basic concepts from probability theory that are applied throughout the text. Chapters 3-7 discuss the solution of the canonical inverse problem, that is, the linear problem with Gaussian statistics, and discussions on problems that are non-Gaussian and nonlinear are covered in Chapters 8 and 9. Chapters 10-12 present examples of the use of inverse theory and a discussion on the numerical algorithms that must be employed to solve inverse problems on a computer. This book is of value to graduate students and many college seniors in the applied sciences.

Download or read book MATLAB® Recipes for Earth Sciences written by Martin H. Trauth and published by Springer Science & Business Media. This book was released on 2007 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces methods of data analysis in geosciences using MATLAB such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis and the application of linear time-invariant and adaptive filters. Includes a brief description of each method and numerous examples demonstrating how MATLAB can be used on data sets from earth sciences.

Download or read book Environmental Data Analysis with MatLab or Python written by William Menke and published by Academic Press. This book was released on 2022-08-14 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Environmental Data Analysis with MATLAB, Third Edition, is a new edition that expands fundamentally on the original with an expanded tutorial approach, more clear organization, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios, including case studies in each chapter. The new edition is expanded to include applications to Python, an open source software environment. Significant content in Environmental Data Analysis with MATLAB, Third Edition is devoted to teaching how the programs can be effectively used in an environmental data analysis setting. This new edition offers chapters that can both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate relevant use cases. - Provides a clear learning path for researchers and students using data analysis techniques which build upon one another, choosing the right order of presentation to substantially aid the reader in learning material - Includes crib sheets to summarize the most important data analysis techniques, results, procedures, and formulas and worked examples to demonstrate techniques - Uses real-world environmental examples and case studies formulated using the readily-available software environment in both MATLAB® and Python - Completely updated and expanded to include coverage of Python and reorganized for better navigability - Includes access to both an instructor site with exemplary lectures and solutions to problems and a supplementary site with MATLAB LiveScripts and Python Notebooks

Download or read book Practical Data Analysis in Chemistry written by Marcel Maeder and published by Elsevier. This book was released on 2007-08-10 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses.* Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes.* Provides examples of routines that are easily adapted to the processes investigated by the reader* 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered