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| artificial intelligence in environmental science: 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 science: 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 science: 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 science: Artificial Intelligence and The Environment Cindy Mason, 2020-07-07 This volume reports 16 AI projects on engineering sustainability using AI, Machine Learning, Signal Processing, Databases and other Technologies (Hybrid AI). Sixty scientists contribute to the volume on ‘Boots on the Ground’ topics including fire fighting, forestry sustainability, flood prediction, algae bloom prediction, water pollution prediction, sewage treatment, recycling and resource consumption. There are also ‘Data, Data Everywhere’ topics including biodiversity cataloguing, plant physiology and climate modeling, forest ecosystem modelling, satellite data aggregation and viewing and weather forecasting. The contributions of each team of scientists, AI researchers and engineers has been assembled with a set of helpful questions and answers called “Classroom Connection” at the end of each chapter. The existence of this book serves to document the AI projects in existence and some of the people who have been actively working to create sustainability using AI. Inside you’ll find many examples of hybrid AI - systems so complex, they need every AI trick in the book to solve them, and then some. The book is presented at the 2019 U.N. Climate Summit in Madrid Spain.--Publisher's description. |
| artificial intelligence in environmental science: 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 science: 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 science: 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 science: Artificial Intelligence and Conservation Fei Fang, Milind Tambe, Bistra Dilkina, Andrew J. Plumptre, 2019-03-28 With the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral reefs, rivers, and other natural resources. It presents how the joint efforts of researchers in computer science, ecology, economics, and psychology help address the goals of the United Nations' 2030 Agenda for Sustainable Development. Written at a level accessible to conservation professionals and AI researchers, the book offers both an overview of the field and an in-depth view of how AI is being used to understand patterns in wildlife poaching and enhance patrol efforts in response, covering research advances, field tests and real-world deployments. The book also features efforts in other major conservation directions, including protecting natural resources, ecosystem monitoring, and bio-invasion management through the use of game theory, machine learning, and optimization. |
| artificial intelligence in environmental science: Artificial Intelligence (AI) S. Kanimozhi Suguna, M. Dhivya, Sara Paiva, 2021-05-27 This book aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of Artificial Intelligence. The book provides a premier interdisciplinary platform to present practical challenges and adopted solutions. The book addresses the complete functional framework workflow in Artificial Intelligence technology. It explores the basic and high-level concepts and can serve as a manual for the industry for beginners and the more advanced. It covers intelligent and automated systems and its implications to the real-world, and offers data acquisition and case studies related to data-intensive technologies in AI-based applications. The book will be of interest to researchers, professionals, scientists, professors, students of computer science engineering, electronics and communications, as well as information technology. |
| artificial intelligence in environmental science: Reshaping Environmental Science Through Machine Learning and IoT Rajeev Kumar Gupta, Arti Jain, John Wang, Rajesh Pateriya, 2024 This book provides a theoretical and practical understanding of IoT, ML, and Artificial Intelligence (AI) technologies to the readers, including their fundamental principles, components, and relevance to environmental sciences-- |
| artificial intelligence in environmental science: The Atlas of AI Kate Crawford, 2021-04-06 The hidden costs of artificial intelligence, from natural resources and labor to privacy and freedom What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind automated services, to the data AI collects from us. Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world. |
| artificial intelligence in environmental science: Artificial Neural Networks in Biological and Environmental Analysis Grady Hanrahan, 2011-01-18 Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound |
| artificial intelligence in environmental science: 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 science: 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 science: Environmental Decision Support Systems Giorgio Guariso, H. Werthner, 1989 |
| artificial intelligence in environmental science: Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture Tomar, Pradeep, Kaur, Gurjit, 2021-01-08 As technology continues to saturate modern society, agriculture has started to adopt digital computing and data-driven innovations. This emergence of “smart” farming has led to various advancements in the field, including autonomous equipment and the collection of climate, livestock, and plant data. As connectivity and data management continue to revolutionize the farming industry, empirical research is a necessity for understanding these technological developments. Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture provides emerging research exploring the theoretical and practical aspects of critical technological solutions within the farming industry. Featuring coverage on a broad range of topics such as crop monitoring, precision livestock farming, and agronomic data processing, this book is ideally designed for farmers, agriculturalists, product managers, farm holders, manufacturers, equipment suppliers, industrialists, governmental professionals, researchers, academicians, and students seeking current research on technological applications within agriculture and farming. |
| artificial intelligence in environmental science: Applications of Computational Science in Artificial Intelligence Nayyar, Anand, Kumar, Sandeep, Agrawal, Akshat, 2022-04-22 Computational science, in collaboration with engineering, acts as a bridge between hypothesis and experimentation. It is essential to use computational methods and their applications in order to automate processes as many major industries rely on advanced modeling and simulation. Computational science is inherently interdisciplinary and can be used to identify and evaluate complicated systems, foresee their performance, and enhance procedures and strategies. Applications of Computational Science in Artificial Intelligence delivers technological solutions to improve smart technologies architecture, healthcare, and environmental sustainability. It also provides background on key aspects such as computational solutions, computation framework, smart prediction, and healthcare solutions. Covering a range of topics such as high-performance computing and software infrastructure, this reference work is ideal for software engineers, practitioners, researchers, scholars, academicians, instructors, and students. |
| artificial intelligence in environmental science: Smart Cities and Artificial Intelligence Christopher Grant Kirwan, Fu Zhiyong, 2020-05-05 Smart Cities and Artificial Intelligence offers a comprehensive view of how cities are evolving as smart ecosystems through the convergence of technologies incorporating machine learning and neural network capabilities, geospatial intelligence, data analytics and visualization, sensors, and smart connected objects. These recent advances in AI move us closer to developing urban operating systems that simulate human, machine, and environmental patterns from transportation infrastructure to communication networks. Exploring cities as real-time, living, dynamic systems, and providing tools and formats including generative design and living lab models that support cities to become self-regulating, this book provides readers with a conceptual and practical knowledge base to grasp and apply the key principles required in the planning, design, and operations of smart cities. Smart Cities and Artificial Intelligence brings a multidisciplinary, integrated approach, examining how the digital and physical worlds are converging, and how a new combination of human and machine intelligence is transforming the experience of the urban environment. It presents a fresh holistic understanding of smart cities through an interconnected stream of theory, planning and design methodologies, system architecture, and the application of smart city functions, with the ultimate purpose of making cities more liveable, sustainable, and self-sufficient. |
| artificial intelligence in environmental science: Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing Ni-Bin Chang, Kaixu Bai, 2018-02-21 In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering. |
| artificial intelligence in environmental science: 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 science: 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 science: Artificial Intelligence and Social Work Milind Tambe, Eric Rice, 2018-11-29 An introductory guide with real-life examples on using AI to help homeless youth, diabetes patients, and other social welfare interventions. |
| artificial intelligence in environmental science: 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 science: 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 science: New Perspectives on Virtual and Augmented Reality Linda Daniela, 2020-05-31 New Perspectives on Virtual and Augmented Reality discusses the possibilities of using virtual and augmented reality in the role of innovative pedagogy, where there is an urgent need to find ways to teach and support learning in a transformed learning environment. Technology creates opportunities to learn differently and presents challenges for education. Virtual reality solutions can be exciting, create interest in learning, make learning more accessible and make learning faster. This book analyses the capabilities of virtual, augmented and mixed reality by providing ideas on how to make learning more effective, how existing VR/AR solutions can be used as learning tools and how a learning process can be structured. The virtual reality (VR) solutions can be used successfully for educational purposes as their use can contribute to the construction of knowledge and the development of metacognitive processes. They also contribute to inclusive education by providing access to knowledge that would not otherwise be available. This book will be of great interest to academics, researchers and post-graduate students in the field of educational technology. |
| artificial intelligence in environmental science: Artificial Intelligence and Global Security Yvonne R. Masakowski, 2020-07-15 Artificial Intelligence and Global Security: Future Trends, Threats and Considerations brings a much-needed perspective on the impact of the integration of Artificial Intelligence (AI) technologies in military affairs. Experts forecast that AI will shape future military operations in ways that will revolutionize warfare. |
| artificial intelligence in environmental science: 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 science: Artificial Intelligence for the Internet of Everything William Lawless, Ranjeev Mittu, Donald Sofge, Ira S S Moskowitz, Stephen Russell, 2019-02-21 Artificial Intelligence for the Internet of Everything considers the foundations, metrics and applications of IoE systems. It covers whether devices and IoE systems should speak only to each other, to humans or to both. Further, the book explores how IoE systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems. It examines the meaning, value and effect that IoT has had and may have on ordinary life, in business, on the battlefield, and with the rise of intelligent and autonomous systems. Based on an artificial intelligence (AI) perspective, this book addresses how IoE affects sensing, perception, cognition and behavior. Each chapter addresses practical, measurement, theoretical and research questions about how these things may affect individuals, teams, society or each other. Of particular focus is what may happen when these things begin to reason, communicate and act autonomously on their own, whether independently or interdependently with other things. - Considers the foundations, metrics and applications of IoE systems - Debates whether IoE systems should speak to humans and each other - Explores how IoE systems affect targeted audiences and society - Discusses theoretical IoT ecosystem models |
| artificial intelligence in environmental science: Deep Learning for the Earth Sciences Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein, 2021-08-18 DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists. |
| artificial intelligence in environmental science: Watershed Management and Applications of AI Sandeep Samantaray, Abinash Sahoo, Dillip K. Ghose, 2021-05-16 Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations. Features: Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques Provides analysis of land erosion and sediment transport in watersheds from small to large scale Includes estimations for runoff using different methodologies with systematic approaches for each Discusses water harvesting and development of water yield catchments This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields. |
| artificial intelligence in environmental science: Artificial Intelligence and Industry 4.0 Aboul Ella Hassanien, Jyotir Moy Chatterjee, Vishal Jain, 2022-08-14 Artificial Intelligence and Industry 4.0 explores recent advancements in blockchain technology and artificial intelligence (AI) as well as their crucial impacts on realizing Industry 4.0 goals. The book explores AI applications in industry including Internet of Things (IoT) and Industrial Internet of Things (IIoT) technology. Chapters explore how AI (machine learning, smart cities, healthcare, Society 5.0, etc.) have numerous potential applications in the Industry 4.0 era. This book is a useful resource for researchers and graduate students in computer science researching and developing AI and the IIoT. - Explores artificial intelligence applications within the industrial manufacturing and communications sectors - Presents a wide range of machine learning, computer vision, and digital twin applications across the IoT sector - Explores how deep learning and cognitive computing tools enable processing vast data sets, precise and comprehensive forecast of risks, and delivering recommended actions |
| artificial intelligence in environmental science: Understanding the Role of Artificial Intelligence and Its Future Social Impact Sheikh, Salim, 2020-07-17 The influence of AI is beginning to filter into every aspect of life, spanning across education, healthcare, business, and more. However, as its prevalence grows, challenges must be addressed including AI replication and even exacerbation of human bias and discrimination and the development of policies and laws that appropriately regulate AI. Stakeholders from all sectors of society need to collaborate on co-designing innovative, agile frameworks for governing AI that allow for its continued adoption while minimizing risk and reducing disruption. Understanding the Role of Artificial Intelligence and Its Future Social Impact is a pivotal reference source that provides vital research on the application of AI within contemporary society and comprehends the future effects of this technology within modern civilization. While highlighting topics such as cognitive computing, ethical issues, and robotics, this publication explores the possible consequences of AI adoption as well as its disruption within industries and emerging markets. This book is ideally designed for researchers, developers, strategists, managers, practitioners, executives, analysts, scientists, policymakers, academicians, and students seeking current research on the future of AI and its influence on the global culture and society. |
| artificial intelligence in environmental science: Artificial Intelligence Margaret A. Boden, 2018-08-13 The applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle. As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. In this Very Short Introduction , Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate how human and animal minds are possible. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable. |
| artificial intelligence in environmental science: Artificial Intelligence Science And Technology - Proceedings Of The 2016 International Conference (Aist2016) Hui Yang, 2017-06-28 The 2016 International Conference on Artificial Intelligence Science and Technology (AIST2016) was held in Shanghai, China, from 15th to 17th July, 2016.AIST2016 aims to bring together researchers, engineers, and students to the areas of Artificial Intelligence Science and Technology. AIST2016 features unique mixed topics of artificial intelligence and application, computer and software, communication and network, information and security, data mining, and optimization.This volume consists of 101 peer-reviewed articles by local and foreign eminent scholars which cover the frontiers and state-of-art development in AI Technology. |
| artificial intelligence in environmental science: AI-Based Services for Smart Cities and Urban Infrastructure Lyu, Kangjuan, Hu, Min, Du, Juan, Sugumaran, Vijayan, 2020-09-04 Cities are the next frontier for artificial intelligence to permeate. As smart urban environments become possible, probable, and even preferred, artificial intelligence offers the chance for even further advancement through infrastructure and industry boosting. Opportunity overflows, but without thorough research to guide a complicated development and implementation process, urban environments can become disorganized and outright dangerous for citizens. AI-Based Services for Smart Cities and Urban Infrastructure is a collection of innovative research that explores artificial intelligence (AI) applications in urban planning. In addition, the book looks at how the internet of things and AI can work together to enable a real smart city and discusses state-of-the-art techniques in urban infrastructure design, construction, operation, maintenance, and management. While highlighting a broad range of topics including construction management, public transportation, and smart agriculture, this book is ideally designed for engineers, entrepreneurs, urban planners, architects, policymakers, researchers, academicians, and students. |
| artificial intelligence in environmental science: Artificial Intelligence for Sustainable Value Creation Pagani, Margherita, Champion, Renaud, 2021-09-07 Artificial Intelligence for Sustainable Value Creation provides a detailed and insightful exploration of both the possibilities and the challenges that accompany widespread Artificial Intelligence |
| artificial intelligence in environmental science: Artificial Intelligence Theory, Models, and Applications P Kaliraj, T. Devi, 2021-10-21 This book examines the fundamentals and technologies of Artificial Intelligence (AI) and describes their tools, challenges, and issues. It also explains relevant theory as well as industrial applications in various domains, such as healthcare, economics, education, product development, agriculture, human resource management, environmental management, and marketing. The book is a boon to students, software developers, teachers, members of boards of studies, and researchers who need a reference resource on artificial intelligence and its applications and is primarily intended for use in courses offered by higher education institutions that strive to equip their graduates with Industry 4.0 skills. FEATURES: Gender disparity in the enterprises involved in the development of AI-based software development as well as solutions to eradicate such gender bias in the AI world A general framework for AI in environmental management, smart farming, e-waste management, and smart energy optimization The potential and application of AI in medical imaging as well as the challenges of AI in precision medicine AI’s role in the diagnosis of various diseases, such as cancer and diabetes The role of machine learning models in product development and statistically monitoring product quality Machine learning to make robust and effective economic policy decisions Machine learning and data mining approaches to provide better video indexing mechanisms resulting in better searchable results ABOUT THE EDITORS: Prof. Dr. P. Kaliraj is Vice Chancellor at Bharathiar University, Coimbatore, India. Prof. Dr. T. Devi is Professor and Head of the Department of Computer Applications, Bharathiar University, Coimbatore, India. |
| artificial intelligence in environmental science: High-Tech and Micropropagation I Y. P. S. Bajaj, 2012-12-06 Presented here is another classic from this series and deals with general aspects of micropropagation of plants for commercial exploitation. It includes chapters on setting up a commercial laboratory, meristem culture, somatic embryogenesis, factors affecting micropropagation, disposable vessels, vitrification, acclimatization, induction of rooting, artificial substrates, cryopreservation and artificial seed. Special emphasis is given on modern approaches and developing technologies such as automation and bioreactors, robots in transplanting, artificial intelligence, information management and computerized greenhouses for en masse commercial production of plants. |
| artificial intelligence in environmental science: Sustainability Rao Y. Surampalli, Tian C. Zhang, Manish Kumar Goyal, Satinder K. Brar, R. D. Tyagi, 2020-03-19 A comprehensive resource to sustainability and its application to the environmental, industrial, agricultural and food security sectors Sustainability fills a gap in the literature in order to provide an important guide to the fundamental knowledge and practical applications of sustainability in a wide variety of areas. The authors – noted experts who represent a number of sustainability fields – bring together in one comprehensive volume the broad range of topics including basic concepts, impact assessment, environmental and the socio-economic aspects of sustainability. In addition, the book covers applications of sustainability in environmental, industrial, agricultural and food security, as well as carbon cycle and infrastructural aspects. Sustainability addresses the challenges the global community is facing due to population growth, depletion of non-renewable resources of energy, environmental degradation, poverty, excessive generation of wastes and more. Throughout the book the authors discuss the economics, ecological, social, technological and systems perspectives of sustainability. This important resource: Explores the fundamentals as well as the key concepts of sustainability; Covers basic concepts, impact assessment, environmental and socio-economic aspects, applications of sustainability in environmental, industrial, agricultural and food security, carbon cycle and infrastructural aspects; Argues the essentiality of sustainability in ensuring the propitious future of earth systems; and Authored by experts from a range of various fields related to sustainability. Written for researchers and scientists, students and academics, Sustainability: Fundamentals and Applications is a comprehensive book that covers the basic knowledge of the topic combined with practical applications. |
| artificial intelligence in environmental science: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
The History and Practice of AI in the Environmental Sciences
This paper tells the story of the evolution of AI in the field through the lens of the AMS Committee on Artificial Intelligence Applications to Environmental Science. The environmental sci-ences …
The Role of Artificial Intelligence in Environmental Monitoring …
In the quest to safeguard Earth's delicate ecosystems, a formidable ally has emerged in the form of Artificial Intelligence (AI). This alliance between technology and environmental science holds …
Artificial Intelligence (AI) for Environmental Sustainability: A ...
ddressing various environmental sustainability challenges through technological innovations in the fields of energy, transportation, bi. diversity, and water management. Thus, the present study...
The role of artificial intelligence in environmental sustainability
Artificial Intelligence (AI) can play a key role in achieving environmental sustainability. The aim of the study is to investigate the advantages and disadvantages of using AI applications in main …
EDS_2200005 1..15 - Cambridge University Press & Assessment
This position paper discusses the need for the environmental sciences community to ensure that they are developing and using artificial intelligence (AI) methods in an ethical and responsible …
AI for a Greener Future: Applications of Artificial Intelligence in ...
Artificial Intelligence is revolutionizing the way we approach environmental sustainability. By providing tools for efficient resource management, climate prediction, waste reduction and …
Interdisciplinary Perspectives: Fusing Artificial Intelligence with ...
May 1, 2024 · The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for …
Analyzing the Potential of Artificial Intelligence and Computer …
Artificial Intelligence (AI) has demonstrated remarkable utility in addressing environmental challenges, particularly in areas such as energy consumption prediction, biodiversity …
Trustworthy Artificial Intelligence for Environmental Sciences
The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) seeks to address such problems by developing synergistic approaches …
Trustworthy Artificial Intelligence for Environmental
Jun 15, 2023 · cannot be cleanly isolated and solved with a single ’correct’ answer (e.g., Rittel 1973; Wirz 2021). The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, …
IMPACT OF ENVIRONMENTAL SUSTAINABILITY AND …
This paper explores the applications of AI in environmental monitoring, conservation, and sustainability. We discuss the opportunities and challenges associated with AI adoption, …
AI-Powered Environmental Monitoring and Conservation …
Artificial Intelligence (AI) improves environmental assessment accuracy, permits prompt interventions, and facilitates more dynamic and responsive conservation initiatives.
History and Potential of Artificial Intelligence for the …
Mar 25, 2020 · The field of Artificial Intelligence (AI), including applications in the environmental sciences, is evolving at an accelerating pace. Its progress has been made possible by …
The Impact of Artificial Intelligence on Environmental Protection
In the face of escalating global environmental issues, the role of artificial intelligence (AI) in environmental protection has become increasingly significant. One of the key areas of focus is...
Environmental Impact of Artificial Intelligence - inria.hal.science
Large AI models have high training costs in terms of energy and carbon emissions generated due to computation and the manufacture of specialized hardware accelerators on which such …
The Role of Artificial Intelligence in Environmental Monitoring …
With the pressing global challenges of climate change, habitat destruction, and biodiversity loss, the integration of artificial intelligence (AI) technologies has emerged as a potent tool in …
Revisit the environmental impact of artificial intelligence: the ...
Revisit the environmental impact of artificial intelligence: the overlooked carbon emission source? Higher Education Press 2024.
Generative Artificial Intelligence: A New Engine for Advancing ...
Sep 30, 2024 · Current and potential applications and existing obstacles of generative AI in environmental science and engineering. the performance of these data-hungry models by …
Interdisciplinary Outlook: Integrating Artificial Intelligence …
Artificial intelligence (AI) can be integrated with environmental science to provide sustainable solutions. AI systems have the potential to detect, monitor, and manage heavy metal pollution,...
Introduction to artificial intelligence and machine learning in ...
studies of environmental science and human health. Advances in AI and ML allow researchers to collect and analyze vast amounts of data, enabling them to better understand complex environ …
The History and Practice of AI in the Environmental Sciences
This paper tells the story of the evolution of AI in the field through the lens of the AMS Committee on Artificial Intelligence Applications to Environmental Science. …
The Role of Artificial Intelligence in Environmenta…
In the quest to safeguard Earth's delicate ecosystems, a formidable ally has emerged in the form of Artificial Intelligence (AI). This alliance between technology and …
Artificial Intelligence (AI) for Environmental Sustainability: …
ddressing various environmental sustainability challenges through technological innovations in the fields of energy, transportation, bi. diversity, and …
The role of artificial intelligence in environmental sustainability
Artificial Intelligence (AI) can play a key role in achieving environmental sustainability. The aim of the study is to investigate the advantages and disadvantages of using AI …
EDS_2200005 1..15 - Cambridge University Press & Assessment
This position paper discusses the need for the environmental sciences community to ensure that they are developing and using artificial intelligence (AI) methods in an …
