Download or read book Artificial Intelligence in Earth Science written by Ziheng Sun and published by Elsevier. This book was released on 2023-04-27 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth Science. The book focuses on the most challenging problems in applying AI in Earth system sciences, such as training data preparation, model selection, hyperparameter tuning, model structure optimization, spatiotemporal generalization, transforming model results into products, and explaining trained models. In addition, it provides full-stack workflow tutorials to help walk readers through the whole process, regardless of previous AI experience. The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work. Provides practical, step-by-step guides for Earth Scientists who are interested in implementing AI techniques in their work Features case studies to show real-world examples of techniques described in the book Includes additional elements to help readers who are new to AI, including end-of-chapter, key concept bulleted lists that concisely cover key concepts in the chapter

Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning and Artificial Intelligence in Geosciences written by and published by Academic Press. This book was released on 2020-09-22 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. Provides high-level reviews of the latest innovations in geophysics Written by recognized experts in the field Presents an essential publication for researchers in all fields of geophysics

Download or read book Artificial Intelligence Methods in the Environmental Sciences written by Sue Ellen Haupt and published by Springer Science & Business Media. This book was released on 2008-11-28 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Large Scale Machine Learning in the Earth Sciences written by Ashok N. Srivastava and published by CRC Press. This book was released on 2017-08-01 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Download or read book Novel AI Applications for Advancing Earth Sciences written by Yadav, Sudesh and published by IGI Global. This book was released on 2023-12-29 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Earth Sciences industry faces a new challenge - the need for accurate, efficient, and reliable methods to monitor and predict geological phenomena and environmental changes. As climate change, earthquakes, and other natural disasters become more frequent and severe, the necessity for advanced tools and techniques is paramount. Traditional methods often fall short in providing the precision and speed required to address these critical issues. Geologists and earth scientists who are grappling with the urgent problem of utilizing artificial intelligence (AI) to revolutionize their field, will find the solution within the pages of Novel AI Applications for Advancing Earth Sciences. This book offers the research community concepts expanding upon the fusion of AI technology with earth sciences. By leveraging advanced AI tools, such as convolutional neural networks, support vector machines, artificial neural networks, and the potential of remote sensing satellites, this book transforms the identification of geological features, geological mapping, soil classification, and gas detection. Scientists can now predict earthquakes and assess the probability of climate change with unprecedented accuracy. Additionally, the book explains how the optimization of algorithms for specific tasks substantially reduces the time complexity of earth observations, leading to an unprecedented leap in accuracy and efficiency.

Download or read book Artificial Intelligence and Data Science in Environmental Sensing written by Mohsen Asadnia and published by Academic Press. This book was released on 2022-02-09 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Machine Learning Methods in the Environmental Sciences written by William W. Hsieh and published by Cambridge University Press. This book was released on 2009-07-30 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Download or read book Computers in Earth and Environmental Sciences written by Hamid Reza Pourghasemi and published by Elsevier. This book was released on 2021-09-22 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Artificial Intelligence and the Environmental Crisis written by Keith Ronald Skene and published by Routledge. This book was released on 2019-12-19 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Intelligence Techniques in Earth and Environmental Sciences written by Tanvir Islam and published by Springer Science & Business Media. This book was released on 2014-02-14 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational intelligence techniques have enjoyed growing interest in recent decades among the earth and environmental science research communities for their powerful ability to solve and understand various complex problems and develop novel approaches toward a sustainable earth. This book compiles a collection of recent developments and rigorous applications of computational intelligence in these disciplines. Techniques covered include artificial neural networks, support vector machines, fuzzy logic, decision-making algorithms, supervised and unsupervised classification algorithms, probabilistic computing, hybrid methods and morphic computing. Further topics given treatment in this volume include remote sensing, meteorology, atmospheric and oceanic modeling, climate change, environmental engineering and management, catastrophic natural hazards, air and environmental pollution and water quality. By linking computational intelligence techniques with earth and environmental science oriented problems, this book promotes synergistic activities among scientists and technicians working in areas such as data mining and machine learning. We believe that a diverse group of academics, scientists, environmentalists, meteorologists and computing experts with a common interest in computational intelligence techniques within the earth and environmental sciences will find this book to be of great value.

Download or read book Machine Learning and Artificial Intelligence to Advance Earth System Science Opportunities and Challenges Proceedings of a Workshop written by National Academies Of Sciences Engineeri and published by National Academies Press. This book was released on 2022-07-13 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Earth system - the atmospheric, hydrologic, geologic, and biologic cycles that circulate energy, water, nutrients, and other trace substances - is a large, complex, multiscale system in space and time that involves human and natural system interactions. Machine learning (ML) and artificial intelligence (AI) offer opportunities to understand and predict this system. Researchers are actively exploring ways to use ML/AI approaches to advance scientific discovery, speed computation, and link scientific communities. To address the challenges and opportunities around using ML/AI to advance Earth system science, the National Academies convened a workshop in February 2022 that brought together Earth system experts, ML/AI researchers, social and behavioral scientists, ethicists, and decision makers to discuss approaches to improving understanding, analysis, modeling, and prediction. Participants also explored educational pathways, responsible and ethical use of these technologies, and opportunities to foster partnerships and knowledge exchange. This publication summarizes the workshop discussions and themes that emerged throughout the meeting.

Download or read book Introduction to Python in Earth Science Data Analysis written by Maurizio Petrelli and published by Springer Nature. This book was released on 2021-09-16 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.

Download or read book Deep Learning for Hydrometeorology and Environmental Science written by Taesam Lee and published by Springer Nature. This book was released on 2021-01-27 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Integrating Data Science and Earth Science written by Laurens M. Bouwer and published by Springer Nature. This book was released on 2022-07-14 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.

Download or read book Artificial Intelligence and The Environment written by Cindy Mason and published by . This book was released on 2020-07-07 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.

Download or read book Intelligence Systems for Earth Environmental and Planetary Sciences written by Hossein Bonakdari and published by Elsevier. This book was released on 2024-07-30 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligence Systems for Earth, Environmental and Planetary Sciences: Methods, Models and Applications provides cutting-edge theory and applications of modern-day artificial intelligence and data science in the Earth, environment, and planetary science fields. The book is divided into three sections: (i) Methods, covering the fundamentals of intelligence systems, along with an introduction to the preparation of datasets; (ii) Models, detailing model development, data assimilation, and techniques in each field; and (iii) Applications, presenting case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives. Intelligence Systems for Earth, Environmental and Planetary Sciences will be of interest to students, academics, and postgraduate professionals in the field of applied sciences, Earth, environmental, and planetary sciences and would also serve as an excellent companion resource to courses studying artificial intelligence applications for theoretical and practical studies in Earth, environmental, and planetary sciences. Facilitates the application of artificial intelligence and data science systems to create comprehensive methodologies for analyzing, processing, predicting, and management strategies in the fields of Earth, environment, and planetary science Developed with an interdisciplinary framework, with an aim to promote artificial intelligence models for real-time Earth systems Includes a section on case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives