Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering write by Shahab Araghinejad. This book was released on 2013-11-26. Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering available in PDF, EPUB and Kindle. “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.
Handbook of Drought and Water Scarcity
Handbook of Drought and Water Scarcity - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Drought and Water Scarcity write by Saeid Eslamian. This book was released on 2017-08-02. Handbook of Drought and Water Scarcity available in PDF, EPUB and Kindle. This volume include over 30 chapters, written by experts from around the world. It examines drought and all of the fundamental principles relating to drought and water scarcity. It includes coverage of the causes of drought, occurences, preparations, drought vulnerability assessments, societal implications, and more.
MATLAB® Recipes for Earth Sciences
MATLAB® Recipes for Earth Sciences - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook MATLAB® Recipes for Earth Sciences write by Martin H. Trauth. This book was released on 2020-12-02. MATLAB® Recipes for Earth Sciences available in PDF, EPUB and Kindle. MATLAB® is used in a wide range of geoscientific applications, e.g. for image processing in remote sensing, for creating and processing digital elevation models, and for analyzing time series. This book introduces readers to MATLAB-based data analysis methods used in the geosciences, including basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data, and image analysis. The revised and updated Fifth Edition includes seven new sections, and the majority of the chapters have been rewritten and significantly expanded. New sections include error analysis, the problem of classical linear regression of log-transformed data, aligning stratigraphic sequences, the Normalized Difference Vegetation Index, Aitchison’s log-ratio transformation, graphical representation of spherical data, and statistics of spherical data. The book also includes numerous examples demonstrating how MATLAB can be used on datasets from the earth sciences. The supplementary electronic material (available online through SpringerLink) contains recipes that include all the MATLAB commands featured in the book and the sample data.
Dynamic Data Assimilation
Dynamic Data Assimilation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dynamic Data Assimilation write by Dinesh G. Harkut. This book was released on 2020-10-28. Dynamic Data Assimilation available in PDF, EPUB and Kindle. Data assimilation is a process of fusing data with a model for the singular purpose of estimating unknown variables. It can be used, for example, to predict the evolution of the atmosphere at a given point and time. This book examines data assimilation methods including Kalman filtering, artificial intelligence, neural networks, machine learning, and cognitive computing.
Advances in Streamflow Forecasting
Advances in Streamflow Forecasting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances in Streamflow Forecasting write by Priyanka Sharma. This book was released on 2021-06-20. Advances in Streamflow Forecasting available in PDF, EPUB and Kindle. Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of statistical and stochastic time-series modelling with their recent developments, stand-alone data-driven approach such as artificial intelligence techniques, and modern hybridized approach where data-driven models are combined with preprocessing methods to improve the forecast accuracy of streamflows and to reduce the forecast uncertainties. This book starts by providing the background information, overview, and advances made in streamflow forecasting. The overview portrays the progress made in the field of streamflow forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for streamflow forecasting along with case studies from different parts of the world. Each chapter provides a flowchart explaining step-by-step methodology followed in applying the data-driven approach in streamflow forecasting. This book addresses challenges in forecasting streamflows by abridging the gaps between theory and practice through amalgamation of theoretical descriptions of the data-driven techniques and systematic demonstration of procedures used in applying the techniques. Language of this book is kept simple to make the readers understand easily about different techniques and make them capable enough to straightforward replicate the approach in other areas of their interest. This book will be vital for hydrologists when optimizing the water resources system, and to mitigate the impact of destructive natural disasters such as floods and droughts by implementing long-term planning (structural and nonstructural measures), and short-term emergency warning. Moreover, this book will guide the readers in choosing an appropriate technique for streamflow forecasting depending upon the given set of conditions. - Contributions from renowned researchers/experts of the subject from all over the world to provide the most authoritative outlook on streamflow forecasting - Provides an excellent overview and advances made in streamflow forecasting over the past more than five decades and covers both traditional and modern data-driven approaches in streamflow forecasting - Includes case studies along with detailed flowcharts demonstrating a systematic application of different data-driven models in streamflow forecasting, which helps understand the step-by-step procedures
