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| artificial intelligence in environmental engineering: Artificial Intelligence Methods in the Environmental Sciences Sue Ellen Haupt, Antonello Pasini, Caren Marzban, 2008-11-28 How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods. |
| artificial intelligence in environmental engineering: Artificial Intelligence and the Environmental Crisis Keith Ronald Skene, 2019-12-19 A radical and challenging book which argues that artificial intelligence needs a completely different set of foundations, based on ecological intelligence rather than human intelligence, if it is to deliver on the promise of a better world. This can usher in the greatest transformation in human history, an age of re-integration. Our very existence is dependent upon our context within the Earth System, and so, surely, artificial intelligence must also be grounded within this context, embracing emergence, interconnectedness and real-time feedback. We discover many positive outcomes across the societal, economic and environmental arenas and discuss how this transformation can be delivered. Key Features: Identifies a key weakness in current AI thinking, that threatens any hope of a better world. Highlights the importance of realizing that systems theory is an essential foundation for any technology that hopes to positively transform our world. Emphasizes the need for a radical new approach to AI, based on ecological systems. Explains why ecosystem intelligence, not human intelligence, offers the best framework for AI. Examines how this new approach will impact on the three arenas of society, environment and economics, ushering in a new age of re-integration. |
| artificial intelligence in environmental engineering: Artificial Intelligence Systems in Environmental Engineering Jamal Mabrouki, Azrour Maroude, Azeem Irshad, 2024 Specialists are paying attention to the growing concern for environmental issues. Artificial Intelligence Systems in Environmental Engineering uses techniques from the field of Artificial Intelligence as the alternative to address problems that are difficult to model by analytical methods. The book provides a general study and introduces a set of techniques for environmental engineering. With a particular focus on climate change and energy policy, it discusses innovative solutions and models that can positively influence and increase the efficiency of resource use and decrease the impact on the environment while developing the well-being of individuals. The book will be of most interest to research students who have a scientific approach and deal with problems related to environmental engineering. |
| artificial intelligence in environmental engineering: Environmental Engineering for the 21st Century National Academies of Sciences, Engineering, and Medicine, National Academy of Engineering, Division on Engineering and Physical Sciences, Division on Earth and Life Studies, Water Science and Technology Board, Ocean Studies Board, NAE Office of Programs, Board on Life Sciences, Board on Environmental Studies and Toxicology, Board on Earth Sciences and Resources, Board on Energy and Environmental Systems, Board on Chemical Sciences and Technology, Board on Atmospheric Sciences and Climate, Board on Agriculture and Natural Resources, Committee on the Grand Challenges and Opportunites in Environmental Engineering for the Twenty-First Century, 2019-03-08 Environmental engineers support the well-being of people and the planet in areas where the two intersect. Over the decades the field has improved countless lives through innovative systems for delivering water, treating waste, and preventing and remediating pollution in air, water, and soil. These achievements are a testament to the multidisciplinary, pragmatic, systems-oriented approach that characterizes environmental engineering. Environmental Engineering for the 21st Century: Addressing Grand Challenges outlines the crucial role for environmental engineers in this period of dramatic growth and change. The report identifies five pressing challenges of the 21st century that environmental engineers are uniquely poised to help advance: sustainably supply food, water, and energy; curb climate change and adapt to its impacts; design a future without pollution and waste; create efficient, healthy, resilient cities; and foster informed decisions and actions. |
| artificial intelligence in environmental engineering: Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering Goncalo Marques, Joshua O. Ighalo, 2022-03-20 Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering merges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected by intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial neural networks, support vector machine, boosted regression tree, simulated annealing, ant colony algorithm, decision tree, immune algorithm, and imperialist competitive algorithm. This book is a fundamental information source because it is the first book to present the foundational reference material in this new research field. Furthermore, it gives a critical overview of the latest cross-domain research findings and technological developments on the recent advances in computer-aided intelligent environmental data engineering. Captures the application of data science and artificial intelligence for a broader spectrum of environmental engineering problems Presents methods and procedures as well as case studies where state-of-the-art technologies are applied in actual environmental scenarios Offers a compilation of essential and critical reviews on the application of data science and artificial intelligence to the entire spectrum of environmental engineering |
| artificial intelligence in environmental engineering: Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering Shahab Araghinejad, 2013-11-26 “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. |
| artificial intelligence in environmental engineering: Machine Learning Methods in the Environmental Sciences William W. Hsieh, 2009-07-30 A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences. |
| artificial intelligence in environmental engineering: Deep Learning for Hydrometeorology and Environmental Science Taesam Lee, Vijay P. Singh, Kyung Hwa Cho, 2021-01-27 This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model. |
| artificial intelligence in environmental engineering: Artificially Intelligent Nanomaterials for Environmental Engineering Peng Wang, Jian Chang, Lianbin Zhang, 2020-02-18 Presents novel, nanotechnology-based solutions for urgent environmental engineering problems Clear and concise from beginning to end, this book focuses on the design and application of artificially intelligent nanomaterials, which help in solving many tangible environmental problems?especially water and air pollution. It lays out the design concepts, major chemical principles, and materials considerations of artificially intelligent nanomaterials for environmental engineering, and provides proof-of-concept examples such as improved filtration membranes, nanofibrous air filters, and molecularly imprinted nanomaterials. Artificially Intelligent Nanomaterials: For Environmental Engineering starts by describing the background of environmental nanotechnology, the rise of Artificial Intelligence (AI), and the current status of AI in environmental engineering. It then looks at: intelligently functional materials and responsive mechanisms; designing filtration membranes with responsive gates; switchable wettability materials for controllable oil/water separation; and self-healing materials for environmental applications. The book continues with chapters that examine: emerging nanofibrous air filters for PM2.5 removal; self-propelled nanomotors for environmental applications; molecular imprinting in wastewater treatment; and emerging synergistically multifunctional and all-in-one nanomaterials and nanodevices in advanced environmental applications. -Presents the state-of-the-art in environmental technology and puts forward bold ideas for its advancement -Addresses global challenges, including all important water and air quality which are critical for human health and a sustainable future -Concentrates on nanotechnology-enabled solutions for pollutant removal from water and air Artificially Intelligent Nanomaterials: For Environmental Engineering is an ideal book for undergraduates, graduates, scientists, and professionals in the fields of environmental science, material science, chemistry, and chemistry engineering. |
| artificial intelligence in environmental engineering: Machine Learning for Civil and Environmental Engineers M. Z. Naser, 2021-08-10 Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers This textbook introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain. Through real-world projects like analysis and design of structural members, optimizing concrete mixtures for site applications, examining concrete cracking via computer vision, evaluating the response of bridges to hazards, and predicating water quality, and energy expenditure in buildings, this textbook offers readers in-depth case studies with solved problems that are commonly faced by civil and environmental engineers. The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques. Professional engineers and engineering students will find value in the step-by-step examples that are accompanied by sample databases and codes for readers to practice with. Written by a highly qualified professional with significant experience in the field, Machine Learning includes valuable information on: The current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspective Supervised vs. unsupervised learning for regression, classification, and clustering problems Details explainable and causal methods for practical engineering problems Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis A framework for machine learning adoption and application, covering key questions commonly faced by practitioners This textbook is a must-have reference for undergraduate/graduate students to learn concepts on the use of machine learning, for scientists/researchers to learn how to integrate machine learning into civil and environmental engineering, and for design/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure. |
| artificial intelligence in environmental engineering: Applications of Artificial Intelligence in Process Systems Engineering Jingzheng Ren, Weifeng Shen, Yi Man, Lichun Dong, 2021-06-05 Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering |
| artificial intelligence in environmental engineering: Artificial Intelligence Systems in Environmental Engineering Jamal Mabrouki, Azrour Maroude, Azeem Irshad, 2024-06-04 Specialists are paying attention to the growing concern for environmental issues. Artificial Intelligence Systems in Environmental Engineering uses techniques from the field of Artificial Intelligence as the alternative to address problems that are difficult to model by analytical methods. The book provides a general study and introduces a set of techniques for environmental engineering. With a particular focus on climate change and energy policy, it discusses innovative solutions and models that can positively influence and increase the efficiency of resource use and decrease the impact on the environment while developing the well-being of individuals. The book will be of most interest to research students who have a scientific approach and deal with problems related to environmental engineering. |
| artificial intelligence in environmental engineering: Environmental Systems Engineering Henry R. Bungay, 2012-12-06 Environmental Systems Engineering explains how to use new computerized tools to tackle problems in systems engineering. This book covers: expert systems, fuzzy logic, networks, process dynamics, control and statistical approaches to systems analysis. Computer simulation, mathematical models, and newer methods that apply artificial intelligence and neural networks to environmental problems are emphasized. Each book topic is supported by an interactive web site featuring computer graphics, teaching games and navigational aids. Topics are developed through the use of computer exercises using practical problems as examples. |
| artificial intelligence in environmental engineering: Artificial Intelligence in Mechanical and Industrial Engineering Kaushik Kumar, Divya Zindani, J. Paulo Davim, 2021-06-20 Artificial Intelligence in Mechanical and Industrial Engineering offers a unified platform for the dissemination of basic and applied knowledge on the integration of artificial intelligence within the realm of mechanical and industrial engineering. The book covers the tools and information needed to build successful careers and a source of knowledge for those working with AI within these domains. The book offers a systematic approach to explicate fundamentals as well as recent advances. It incorporates various case studies for major topics as well as numerous examples. It will also include real-time intelligent automation and associated supporting methodologies and techniques, and cover decision-support systems, as well as applications of Chaos Theory and Fractals. The book will give scientists, researchers, instructors, students, and practitioners the tools and information needed to build successful careers and to be an impetus to advancements in next-generation mechanical and industrial engineering domains. |
| artificial intelligence in environmental engineering: Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering Thendiyath Roshni, Pijush Samui, Dieu Tien Bui, Dookie Kim, Rahman Khatibi, 2022-03-22 Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering illustrates the concepts of risk, reliability analysis, its estimation, and the decisions leading to sustainable development in the field of civil and environmental engineering. The book provides key ideas on risks in performance failure and structural failures of all processes involved in civil and environmental systems, evaluates reliability, and discusses the implications of measurable indicators of sustainability in important aspects of multitude of civil engineering projects. It will help practitioners become familiar with tolerances in design parameters, uncertainties in the environment, and applications in civil and environmental systems. Furthermore, the book emphasizes the importance of risks involved in design and planning stages and covers reliability techniques to discover and remove the potential failures to achieve a sustainable development. - Contains relevant theory and practice related to risk, reliability and sustainability in the field of civil and environment engineering - Gives firsthand experience of new tools to integrate existing artificial intelligence models with large information obtained from different sources - Provides engineering solutions that have a positive impact on sustainability |
| artificial intelligence in environmental engineering: AI in the Wild Peter Dauvergne, 2020-09-15 Examining the potential benefits and risks of using artificial intelligence to advance global sustainability. Drones with night vision are tracking elephant and rhino poachers in African wildlife parks and sanctuaries; smart submersibles are saving coral from carnivorous starfish on Australia's Great Barrier Reef; recycled cell phones alert Brazilian forest rangers to the sound of illegal logging. The tools of artificial intelligence are being increasingly deployed in the battle for global sustainability. And yet, warns Peter Dauvergne, we should be cautious in declaring AI the planet's savior. In AI in the Wild, Dauvergne avoids the AI industry-powered hype and offers a critical view, exploring both the potential benefits and risks of using artificial intelligence to advance global sustainability. |
| artificial intelligence in environmental engineering: Proceedings of the Eighth International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering B. H. V. Topping, 2005 |
| artificial intelligence in environmental engineering: Artificially Intelligent Nanomaterials for Environmental Engineering Peng Wang, Jian Chang, Lianbin Zhang, 2019-11-05 Presents novel, nanotechnology-based solutions for urgent environmental engineering problems Clear and concise from beginning to end, this book focuses on the design and application of artificially intelligent nanomaterials, which help in solving many tangible environmental problems?especially water and air pollution. It lays out the design concepts, major chemical principles, and materials considerations of artificially intelligent nanomaterials for environmental engineering, and provides proof-of-concept examples such as improved filtration membranes, nanofibrous air filters, and molecularly imprinted nanomaterials. Artificially Intelligent Nanomaterials: For Environmental Engineering starts by describing the background of environmental nanotechnology, the rise of Artificial Intelligence (AI), and the current status of AI in environmental engineering. It then looks at: intelligently functional materials and responsive mechanisms; designing filtration membranes with responsive gates; switchable wettability materials for controllable oil/water separation; and self-healing materials for environmental applications. The book continues with chapters that examine: emerging nanofibrous air filters for PM2.5 removal; self-propelled nanomotors for environmental applications; molecular imprinting in wastewater treatment; and emerging synergistically multifunctional and all-in-one nanomaterials and nanodevices in advanced environmental applications. -Presents the state-of-the-art in environmental technology and puts forward bold ideas for its advancement -Addresses global challenges, including all important water and air quality which are critical for human health and a sustainable future -Concentrates on nanotechnology-enabled solutions for pollutant removal from water and air Artificially Intelligent Nanomaterials: For Environmental Engineering is an ideal book for undergraduates, graduates, scientists, and professionals in the fields of environmental science, material science, chemistry, and chemistry engineering. |
| artificial intelligence in environmental engineering: Environmental Decision Support Systems Giorgio Guariso, H. Werthner, 1989 |
| artificial intelligence in environmental engineering: Applications of Artificial Intelligence Techniques in the Petroleum Industry Abdolhossein Hemmati-Sarapardeh, Aydin Larestani, Nait Amar Menad, Sassan Hajirezaie, 2020-08-26 Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. - Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering - Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms - Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input |
| artificial intelligence in environmental engineering: Artificial Intelligence in Construction Engineering and Management Limao Zhang, Yue Pan, Xianguo Wu, Mirosław J. Skibniewski, 2021-06-18 This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally. |
| artificial intelligence in environmental engineering: Artificial Intelligence Applications in Water Treatment and Water Resource Management Shikuku, Victor, 2023-08-25 The emergence of a plethora of water contaminants as a result of industrialization has introduced complexity to water treatment processes. Such complexity may not be easily resolved using deterministic approaches. Artificial intelligence (AI) has found relevance and applications in almost all sectors and academic disciplines, including water treatment and management. AI provides dependable solutions in the areas of optimization, suspect screening or forensics, classification, regression, and forecasting, all of which are relevant for water research and management. Artificial Intelligence Applications in Water Treatment and Water Resource Management explores the different AI techniques and their applications in wastewater treatment and water management. The book also considers the benefits, challenges, and opportunities for future research. Covering key topics such as water wastage, irrigation, and energy consumption, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students. |
| artificial intelligence in environmental engineering: Machine Learning for Civil and Environmental Engineers M. Z. Naser, 2023-07-17 Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers This textbook introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain. Through real-world projects like analysis and design of structural members, optimizing concrete mixtures for site applications, examining concrete cracking via computer vision, evaluating the response of bridges to hazards, and predicating water quality and energy expenditure in buildings, this textbook offers readers in-depth case studies with solved problems that are commonly faced by civil and environmental engineers. The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques. Professional engineers and engineering students will find value in the step-by-step examples that are accompanied by sample databases and codes for readers to practice with. Written by a highly qualified professional with significant experience in the field, Machine Learning includes valuable information on: The current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspective Supervised vs. unsupervised learning for regression, classification, and clustering problems Explainable and causal methods for practical engineering problems Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis A framework for machine learning adoption and application, covering key questions commonly faced by practitioners This textbook is a must-have reference for undergraduate/graduate students to learn concepts on the use of machine learning, for scientists/researchers to learn how to integrate machine learning into civil and environmental engineering, and for design/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure. |
| artificial intelligence in environmental engineering: Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies Krishna Kumar, Ram Shringar Rao, Omprakash Kaiwartya, Shamim Kaiser, Sanjeevikumar Padmanaban, 2022-03-18 Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. - Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment - Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum - Addresses the advanced field of renewable generation, from research, impact and idea development of new applications |
| artificial intelligence in environmental engineering: Artificial Intelligence and Data Science in Environmental Sensing Mohsen Asadnia, Amir Razmjou, Amin Beheshti, 2022-02-09 Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications. - Presents tools, connections and proactive solutions to take sustainability programs to the next level - Offers a practical guide for making students proficient in modern electronic data analysis and graphics - Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery |
| artificial intelligence in environmental engineering: Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering Gebrail Bekdas, Sinan Melih Nigdeli, Melda Yucel, 2019 This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering-- |
| artificial intelligence in environmental engineering: Artificial Intelligence and Expert Systems for Engineers C.S. Krishnamoorthy, S. Rajeev, 2018-04-24 This book provides a comprehensive presentation of artificial intelligence (AI) methodologies and tools valuable for solving a wide spectrum of engineering problems. What's more, it offers these AI tools on an accompanying disk with easy-to-use software. Artificial Intelligence and Expert Systems for Engineers details the AI-based methodologies known as: Knowledge-Based Expert Systems (KBES); Design Synthesis; Design Critiquing; and Case-Based Reasoning. KBES are the most popular AI-based tools and have been successfully applied to planning, diagnosis, classification, monitoring, and design problems. Case studies are provided with problems in engineering design for better understanding of the problem-solving models using the four methodologies in an integrated software environment. Throughout the book, examples are given so that students and engineers can acquire skills in the use of AI-based methodologies for application to practical problems ranging from diagnosis to planning, design, and construction and manufacturing in various disciplines of engineering. Artificial Intelligence and Expert Systems for Engineers is a must-have reference for students, teachers, research scholars, and professionals working in the area of civil engineering design in particular and engineering design in general. |
| artificial intelligence in environmental engineering: Environmental Data Analysis with MatLab William Menke, Joshua Menke, 2011-09-02 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. |
| artificial intelligence in environmental engineering: Applications of Artificial Intelligence in Electrical Engineering Khalid, Saifullah, 2020-03-27 Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies. Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students. |
| artificial intelligence in environmental engineering: Probabilistic Machine Learning for Civil Engineers James-A. Goulet, 2020-04-14 An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws. The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment. |
| artificial intelligence in environmental engineering: Computers in Earth and Environmental Sciences Hamid Reza Pourghasemi, 2021-09-22 Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management addresses the need for a comprehensive book that focuses on multi-hazard assessments, natural and manmade hazards, and risk management using new methods and technologies that employ GIS, artificial intelligence, spatial modeling, machine learning tools and meta-heuristic techniques. The book is clearly organized into four parts that cover natural hazards, environmental hazards, advanced tools and technologies in risk management, and future challenges in computer applications to hazards and risk management. Researchers and professionals in Earth and Environmental Science who require the latest technologies and advances in hazards, remote sensing, geosciences, spatial modeling and machine learning will find this book to be an invaluable source of information on the latest tools and technologies available. - Covers advanced tools and technologies in risk management of hazards in both the Earth and Environmental Sciences - Details the benefits and applications of various technologies to assist researchers in choosing the most appropriate techniques for purpose - Expansively covers specific future challenges in the use of computers in Earth and Environmental Science - Includes case studies that detail the applications of the discussed technologies down to individual hazards |
| artificial intelligence in environmental engineering: Systems Engineering and Artificial Intelligence William F. Lawless, Ranjeev Mittu, Donald A. Sofge, Thomas Shortell, Thomas A. McDermott, 2021-11-02 This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges. |
| artificial intelligence in environmental engineering: Rights for Robots Joshua C. Gellers, 2020-10-26 Bringing a unique perspective to the burgeoning ethical and legal issues surrounding the presence of artificial intelligence in our daily lives, the book uses theory and practice on animal rights and the rights of nature to assess the status of robots. Through extensive philosophical and legal analyses, the book explores how rights can be applied to nonhuman entities. This task is completed by developing a framework useful for determining the kinds of personhood for which a nonhuman entity might be eligible, and a critical environmental ethic that extends moral and legal consideration to nonhumans. The framework and ethic are then applied to two hypothetical situations involving real-world technology—animal-like robot companions and humanoid sex robots. Additionally, the book approaches the subject from multiple perspectives, providing a comparative study of legal cases on animal rights and the rights of nature from around the world and insights from structured interviews with leading experts in the field of robotics. Ending with a call to rethink the concept of rights in the Anthropocene, suggestions for further research are made. An essential read for scholars and students interested in robot, animal and environmental law, as well as those interested in technology more generally, the book is a ground-breaking study of an increasingly relevant topic, as robots become ubiquitous in modern society. The Open Access version of this book, available at http://www.taylorfrancis.com/books/e/ISBN, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. |
| artificial intelligence in environmental engineering: Exploring Ethical Dimensions of Environmental Sustainability and Use of AI Kannan, Hemachandran, Rodriguez, Raul Villamarin, Paprika, Zita Zoltay, Ade-Ibijola, Abejide, 2023-12-07 Exploring Ethical Dimensions of Environmental Sustainability and Use of AI is a comprehensive and insightful book that delves into the ethical implications and challenges that emerge at the intersection of environmental sustainability and the utilization of artificial intelligence (AI). With a focus on key ethical dimensions such as transparency, equity, privacy, autonomy, unintended consequences, and trade-offs, this book aims to provide a thorough understanding of the responsible deployment and development of AI in the realm of environmental sustainability. By addressing the ethical aspects and challenges involved, this book contributes to the development of ethical guidelines and frameworks that align AI technologies with the vision of a sustainable and equitable future. Researchers will find immense value in this book as it offers a holistic exploration of the ethical implications, filling a critical gap in the existing literature. Policymakers can gain valuable insights to inform the creation of ethical guidelines and regulations governing AI use in sustainable initiatives. Practitioners, including professionals working in environmental organizations or technology companies, will acquire practical knowledge to guide their decision-making and implementation of AI-driven solutions. |
| artificial intelligence in environmental engineering: Handbook of Research on Artificial Intelligence Applications in the Aviation and Aerospace Industries Shmelova, Tetiana, Sikirda, Yuliya, Sterenharz, Arnold, 2019-10-11 With the emergence of smart technology and automated systems in today’s world, artificial intelligence (AI) is being incorporated into an array of professions. The aviation and aerospace industry, specifically, is a field that has seen the successful implementation of early stages of automation in daily flight operations through flight management systems and autopilot. However, the effectiveness of aviation systems and the provision of flight safety still depend primarily upon the reliability of aviation specialists and human decision making. The Handbook of Research on Artificial Intelligence Applications in the Aviation and Aerospace Industries is a pivotal reference source that explores best practices for AI implementation in aviation to enhance security and the ability to learn, improve, and predict. While highlighting topics such as computer-aided design, automated systems, and human factors, this publication explores the enhancement of global aviation security as well as the methods of modern information systems in the aeronautics industry. This book is ideally designed for pilots, scientists, engineers, aviation operators, air crash investigators, teachers, academicians, researchers, and students seeking current research on the application of AI in the field of aviation. |
| artificial intelligence in environmental engineering: Computer Science for Environmental Engineering and EcoInformatics Yuanxu Yu, Zhengtao Yu, Jingying Zhao, 2011-07-18 This two-volume set (CCIS 158 and CCIS 159) constitutes the refereed proceedings of the International Workshop on Computer Science for Environmental Engineering and EcoInformatics, CSEEE 2011, held in Kunming, China, in July 2011. The 150 revised full papers presented in both volumes were carefully reviewed and selected from a large number of submissions. The papers are organized in topical sections on computational intelligence; computer simulation; computing practices and applications; ecoinformatics; image processing information retrieval; pattern recognition; wireless communication and mobile computing; artificial intelligence and pattern classification; computer networks and Web; computer software, data handling and applications; data communications; data mining; data processing and simulation; information systems; knowledge data engineering; multimedia applications. |
| artificial intelligence in environmental engineering: Artificial Intelligence and Smart Agriculture Technology Utku Kose, V. B. Surya Prasath, M. Rubaiyat Hossain Mondal, Prajoy Podder, Subrato Bharati, 2022-06-27 This book was created with the intention of informing an international audience about the latest technological aspects for developing smart agricultural applications. As artificial intelligence (AI) takes the main role in this, the majority of the chapters are associated with the role of AI and data analytics components for better agricultural applications. The first two chapters provide alternative, wide reviews of the use of AI, robotics, and the Internet of Things as effective solutions to agricultural problems. The third chapter looks at the use of blockchain technology in smart agricultural scenarios. In the fourth chapter, a future view is provided of an Internet of Things-oriented sustainable agriculture. Next, the fifth chapter provides a governmental evaluation of advanced farming technologies, and the sixth chapter discusses the role of big data in smart agricultural applications. The role of the blockchain is evaluated in terms of an industrial view under the seventh chapter, and the eighth chapter provides a discussion of data mining and data extraction, which is essential for better further analysis by smart tools. The ninth chapter evaluates the use of machine learning in food processing and preservation, which is a critical issue for dealing with issues concerns regarding insufficient foud sources. The tenth chapter also discusses sustainability, and the eleventh chapter focuses on the problem of plant disease prediction, which is among the critical agricultural issues. Similarly, the twelfth chapter considers the use of deep learning for classifying plant diseases. Finally, the book ends with a look at cyber threats to farming automation in the thirteenth chapter and a case study of India for a better, smart, and sustainable agriculture in the fourteenth chapter. This book presents the most critical research topics of today’s smart agricultural applications and provides a valuable view for both technological knowledge and ability that will be helpful to academicians, scientists, students who are the future of science, and industrial practitioners who collaborate with academia. |
| artificial intelligence in environmental engineering: Artificial Intelligence Applications for Sustainable Construction Moncef L. Nehdi, Harish Chandra Arora, Krishna Kumar, Robertas Damaševičius, Aman Kumar, 2024-02-13 Artificial Intelligence Applications for Sustainable Construction presents the latest developments in AI and ML technologies applied to real-world civil engineering concerns. With an increasing amount of attention on the environmental impact of every industry, more construction projects are going to require sustainable construction practices. This volume offers research evidence, simulation results, and case studies to support this change. Sustainable construction, in fact, not only uses renewable and recyclable materials when building new structures or repairing deteriorating ones, but also adopts all possible methods to reduce energy consumption and waste. The concisely written but comprehensive, practical knowledge put forward by this international group of highly specialized editors and contributors will prove to be beneficial to engineering students and professionals alike. - Presents convincing success stories that encourage application of AI-powered tools to civil engineering - Provides a wealth of valuable technical information to address and resolve many challenging construction problems - Illustrates the most recent shifts in thinking and practice for sustainable construction |
| artificial intelligence in environmental engineering: AI for the Sustainable Development Goals Henrik Skaug Sætra, 2022-02-23 What is artificial intelligence? What are the Sustainable Development Goals (SDGs)? How does AI affect the SDGs? Artificial Intelligence has a real impact on our lives and on our environment, and the Sustainable Development Goals enable us to evaluate these impacts in a systematic manner. This book shows that doing so requires us to understand the context of AI – the infrastructure it is built on, who develops it, who owns it, who has access to it, who uses it, and what it is used for – rather than relying on an isolationist theory of technology. By doing so, we can analyze not only the direct effects of AI on sustainability, but also the indirect – or second-order – effects. AI for the Sustainable Development Goals shows how AI potentially affects all SDGs – both positively and negatively. |
| artificial intelligence in environmental engineering: Artificial Intelligence for Future Society Vasile Palade, |
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation and usage ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
artificial - Wiktionary, the free dictionary
6 days ago · artificial (comparative more artificial, superlative most artificial) Man-made; made by humans; of artifice. The flowers were artificial, and he thought them rather tacky. An artificial …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
Artificial Intelligence Is Not Intelligent - The Atlantic
Jun 6, 2025 · The good news is that nothing about this is inevitable: According to a study released in April by the Pew Research Center, although 56 percent of “AI experts” think artificial …
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation and usage ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
artificial - Wiktionary, the free dictionary
6 days ago · artificial (comparative more artificial, superlative most artificial) Man-made; made by humans; of artifice. The flowers were artificial, and he thought them rather tacky. An artificial …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
Artificial Intelligence Is Not Intelligent - The Atlantic
Jun 6, 2025 · The good news is that nothing about this is inevitable: According to a study released in April by the Pew Research Center, although 56 percent of “AI experts” think artificial …
