Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering

Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering

Author: Shahab Araghinejad

Publisher: Springer Science & Business Media

Published: 2013-11-26

Total Pages: 299

ISBN-13: 9400775067

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“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.


Data-driven Modeling for Sustainable Engineering

Data-driven Modeling for Sustainable Engineering

Author: Kondo H. Adjallah

Publisher:

Published: 2020

Total Pages:

ISBN-13: 9783030136994

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This book gathers the proceedings of the 1st International Conference on Engineering, Applied Sciences and System Modeling (ICEASSM), a four-day event (18th–21st April 2017) held in Accra, Ghana. It focuses on research work promoting a better understanding of engineering problems through applied sciences and modeling, and on solutions generated in an African setting but with relevance to the world as a whole. The book provides a holistic overview of challenges facing Africa, and addresses various areas from research and development perspectives. Presenting contributions by scientists, engineers and experts hailing from a host of international institutions, the book offers original approaches and technological solutions to help solve real-world problems through research and knowledge sharing. Further, it explores promising opportunities for collaborative research on issues of scientific, economic and social development, making it of interest to researchers, scientists and practitioners looking to conduct research in disciplines such as water supply, control, civil engineering, statistical modeling, renewable energy and sustainable urban development.


Data-Driven Modeling for Sustainable Engineering

Data-Driven Modeling for Sustainable Engineering

Author: Kondo H. Adjallah

Publisher: Springer

Published: 2019-06-21

Total Pages: 420

ISBN-13: 3030136973

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This book gathers the proceedings of the 1st International Conference on Engineering, Applied Sciences and System Modeling (ICEASSM), a four-day event (18th–21st April 2017) held in Accra, Ghana. It focuses on research work promoting a better understanding of engineering problems through applied sciences and modeling, and on solutions generated in an African setting but with relevance to the world as a whole. The book provides a holistic overview of challenges facing Africa, and addresses various areas from research and development perspectives. Presenting contributions by scientists, engineers and experts hailing from a host of international institutions, the book offers original approaches and technological solutions to help solve real-world problems through research and knowledge sharing. Further, it explores promising opportunities for collaborative research on issues of scientific, economic and social development, making it of interest to researchers, scientists and practitioners looking to conduct research in disciplines such as water supply, control, civil engineering, statistical modeling, renewable energy and sustainable urban development.


Data-Driven Science and Engineering

Data-Driven Science and Engineering

Author: Steven L. Brunton

Publisher: Cambridge University Press

Published: 2022-05-05

Total Pages: 615

ISBN-13: 1009098489

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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.


Data-driven Analytics for Sustainable Buildings and Cities

Data-driven Analytics for Sustainable Buildings and Cities

Author: Xingxing Zhang

Publisher: Springer Nature

Published: 2021-09-11

Total Pages: 450

ISBN-13: 9811627789

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This book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behavior, thermal comfort, air quality and economic modelling across levels of building, communities and cities, through various data analytical approaches. It highlights the complex interplay of heating/cooling, ventilation and power systems in different processes, such as design, renovation and operation, for buildings, communities and cities. Methods from classical statistics, machine learning and artificial intelligence are applied into analyses for different building/urban components and systems. Knowledge from this book assists to accelerate sustainability of the society, which would contribute to a prospective improvement through data analysis in the liveability of both built and urban environment. This book targets a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it appeals to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.


Water Engineering Modeling and Mathematic Tools

Water Engineering Modeling and Mathematic Tools

Author: Pijush Samui

Publisher: Elsevier

Published: 2021-02-05

Total Pages: 592

ISBN-13: 0128208775

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Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering. This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering. - Includes firsthand experience about artificial intelligence models, utilizing case studies - Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow - Presents the application of new instruments in water engineering


Data Science Applied to Sustainability Analysis

Data Science Applied to Sustainability Analysis

Author: Jennifer Dunn

Publisher: Elsevier

Published: 2021-05-11

Total Pages: 312

ISBN-13: 0128179775

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Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses


Model Calibration and Parameter Estimation

Model Calibration and Parameter Estimation

Author: Ne-Zheng Sun

Publisher: Springer

Published: 2015-07-01

Total Pages: 638

ISBN-13: 1493923234

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This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection design. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can be used as a reference for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for professionals such as petroleum engineers, mining engineers, chemists, mechanical engineers, biologists, biology and medical engineering, applied mathematicians, and others who perform mathematical modeling.


Modeling Tools for Environmental Engineers and Scientists

Modeling Tools for Environmental Engineers and Scientists

Author: Nirmala Khandan

Publisher: CRC Press

Published: 2001-12-20

Total Pages: 322

ISBN-13: 1420003399

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Modeling Tools for Environmental Engineers and Scientists enables environmental professionals, faculty, and students with minimal computer programming skills to develop computer-based mathematical models for natural and engineered environmental systems. The author illustrates how commercially available syntax-free authoring software can be adapted


Engineering and Sustainable Community Development

Engineering and Sustainable Community Development

Author: Juan C. Lucena

Publisher: Morgan & Claypool Publishers

Published: 2009-08-15

Total Pages: 216

ISBN-13: 1608450708

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This book, Engineering and Sustainable Community Development, presents an overview of engineering as it relates to humanitarian engineering, service learning engineering, or engineering for community development, often called sustainable community development (SCD). The topics covered include a history of engineers and development, the problems of using industry-based practices when designing for communities, how engineers can prepare to work with communities, and listening in community development. It also includes two case studies -- one of engineers developing a windmill for a community in India, and a second of an engineer "mapping communities" in Honduras to empower people to use water effectively -- and student perspectives and experiences on one curricular model dealing with community development. Table of Contents: Introduction / Engineers and Development: From Empires to Sustainable Development / Why Design for Industry Will Not Work as Design for Community / Engineering with Community / Listening to Community / ESCD Case Study 1: Sika Dhari's Windmill / ESCD Case Study 2: Building Organizations and Mapping Communities in Honduras / Students' Perspectives on ESCD: A Course Model / Beyond Engineers and Community: A Path Forward