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Author: Shahab Araghinejad
Publisher: Springer Science & Business Media
ISBN: 9400775067
Category : Science
Languages : en
Pages : 299
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Book Description
“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.
Author: Shahab Araghinejad
Publisher: Springer Science & Business Media
ISBN: 9400775067
Category : Science
Languages : en
Pages : 299
Get Book Here
Book Description
“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.
Author: Saeid Eslamian
Publisher: CRC Press
ISBN: 1315404206
Category : Science
Languages : en
Pages : 750
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Book Description
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.
Author: Martin H. Trauth
Publisher: Springer Nature
ISBN: 3030384411
Category : Science
Languages : en
Pages : 526
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Book Description
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.
Author: Dinesh G. Harkut
Publisher: BoD – Books on Demand
ISBN: 1839680830
Category : Computers
Languages : en
Pages : 120
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Book Description
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.
Author: Priyanka Sharma
Publisher: Elsevier
ISBN: 0128209240
Category : Science
Languages : en
Pages : 406
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Book Description
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
Author: Borko Furht
Publisher: Springer
ISBN: 3319445502
Category : Computers
Languages : en
Pages : 405
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Book Description
The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems®) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.
Author: Jay Doorga
Publisher: Springer Nature
ISBN: 3030952134
Category : Technology & Engineering
Languages : en
Pages : 84
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Book Description
This book provides a comprehensive guide on how geographic information systems (GIS) can be used to optimize solar energy resources. A collection of the latest cutting-edge research is presented which seeks to address the most pressing issues faced by policymakers regarding the planning and exploitation of solar energy. Scientifically robust models are developed to guide researchers on identifying optimum sites for the implementation of solar energy projects. Each methodology presented is accompanied by global case studies, ranging from the small islands of Hawaii and Mauritius to larger countries such as India and Spain. This book is primarily targeted to researchers aspiring to unveil and optimize the solar resource potential of their countries for the benefit of a wider audience, ranging from architects, agro-industrialists, climatologists, and energy experts.
Author: Ali Asghar Rastegari
Publisher: Elsevier
ISBN: 012820527X
Category : Technology & Engineering
Languages : en
Pages : 368
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Book Description
New and Future Developments in Microbial Biotechnology and Bioengineering: Trends of Microbial Biotechnology for Sustainable Agriculture and Biomedicine Systems: Diversity and Functional Perspectives describes how specific techniques can be used to generalize the metabolism of bacteria that optimize biologic improvement strategies and bio-transport processes. Microbial biotechnology focuses on microbes of agricultural, environmental, industrial, and clinical significance. This volume discusses several methods based on molecular genetics, systems, and biology of synthetic, genomic, proteomic, and metagenomics. Recent developments in our understanding of the role of microbes in sustainable agriculture and biotechnology have created a highly potential research area. The soil and plant microbiomes have a significant role in plant growth promotion, crop yield, soil health and fertility for sustainable developments. The microbes provide nutrients and stimulate plant growth through different mechanisms, including solubilization of phosphorus, potassium, and zinc; biological nitrogen fixation; production of siderophore, ammonia, HCN and other secondary metabolites which are antagonistic against pathogenic microbes. This new book provides an indispensable reference source for engineers/bioengineers, biochemists, biotechnologists, microbiologists, agrochemists, and researchers who want to know about the unique properties of this microbe and explore its sustainable agriculture future applications. - Introduces the principles of microbial biotechnology and its application in plant growth and soil health for sustainable agriculture - Explores various plant microbiomes and their beneficial impact on plant growth for crop improvement - Explains the mechanisms of plant-microbe interaction and plant growth promotion - Includes current applications of microbial consortium for enhance production of crop in eco-friendly manners
Author: Mohammad Noori
Publisher: CRC Press
ISBN: 1000965554
Category : Technology & Engineering
Languages : en
Pages : 358
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Book Description
Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.
Author: Hossein Bonakdari
Publisher: Elsevier
ISBN: 0323972756
Category : Science
Languages : en
Pages : 372
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Book Description
Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software allows for new avenues in time series analysis and predictive modeling which summarize more than ten years of experience in the application of stochastic models in environmental problems. The book introduces a variety of different topics in time series in the modeling and prediction of complex environmental systems. Most importantly, all codes are user-friendly and readers will be able to use them for their cases. Users who may not be familiar with MATLAB software can also refer to the appendix. This book also guides the reader step-by-step to learn developed codes for time series modeling, provides required toolboxes, explains concepts, and applies different tools for different types of environmental time series problems. - Provides video tutorials on the use of codes - Includes a companion site with 3,000 lines of programming, 70 principal codes and 100 pseudo codes - Highlights multiple methods to Illustrate each problem
