Download or read book Machine Learning for Planetary Science written by Joern Helbert and published by Elsevier. This book was released on 2022-03-22 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Download or read book Machine Learning Techniques for Space Weather written by Enrico Camporeale and published by Elsevier. This book was released on 2018-05-31 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms

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 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

Download or read book Machine Learning in Earth Environmental and Planetary Sciences written by Hossein Bonakdari and published by Elsevier. This book was released on 2023-07-03 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation. This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results. Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes Includes numerous figures, illustrations and tables to help readers better understand the concepts covered

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 Machine Learning in Heliophysics written by Thomas Berger and published by Frontiers Media SA. This book was released on 2021-11-24 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 The Atlas of AI written by Kate Crawford and published by Yale University Press. This book was released on 2021-04-06 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science written by Emma Torres Chickles and published by . This book was released on 2021 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale astronomical surveys and planetary missions have produced huge amounts of data. The exponential growth in data volume has allowed the application of novel data science techniques, including machine learning. We use machine learning methods to analyze and extract new information from two enormous datasets: images of impact craters captured by the Mars Reconnaissance Orbiter (MRO) and time-series data collected by the Transiting Exoplanet Survey Satellite (TESS). Using images of impact craters captured by the MRO, we infer the spatial variation in the retention of ejecta deposits on Mars. We do this by training a convolutional neural network (CNN) to detect the presence of ejecta deposits around small craters. Our machine learning method to detect pre- served ejecta deposits will enable the study of the processes driving landscape evolution on Mars. In a methodologically relevant but independent study, we conduct a census of different types of variability of nearby stars using photometric time-series data produced by TESS. We do this by extracting representational features from light curves using a convolutional autoencoder and clustering these features. Our unsupervised machine learning method will accelerate the augmentation of variable star catalogues, which are essential for studies of stellar magnetism, stellar evolution, and the habitability of hosted exoplanets.

Download or read book Machine Learning and Data Science in the Power Generation Industry written by Patrick Bangert and published by Elsevier. This book was released on 2021-01-14 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

Download or read book Machine Learning for Small Bodies in the Solar System written by Valerio Carruba and published by Elsevier. This book was released on 2025-01-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Small Bodies in the Solar System provides the latest developments and methods in applications of Machine Learning (ML) and Artificial Intelligence (AI) to different aspects of Solar System bodies, including dynamics, physical properties, detection algorithms, etc. Allowing readers to apply ML and AI to the study of asteroids, comets, moons, and Trans-Neptunian Objects. The practical approach encompasses a wide range of topics, providing both experienced and novice researchers with essential tools and insights. The inclusion of codes and links to publicly available repositories further facilitates hands-on learning, enabling readers to put their newfound knowledge into practice. Machine Learning for Small Bodies in the Solar System serves as an invaluable reference for researchers working into the broad fields of Solar System bodies; both seasoned researchers seeking to enhance their understanding of ML and AI in the context of Solar System exploration or those just stepping into the field looking for direction on Methodologies and techniques to apply ML and AI methodologies.

Download or read book Knowledge Discovery in Big Data from Astronomy and Earth Observation written by Petr Skoda and published by Elsevier. This book was released on 2020-04-10 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

Download or read book Introduction to Planetary Science written by Gunter Faure and published by Springer Science & Business Media. This book was released on 2007-05-04 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook details basic principles of planetary science that help to unify the study of the solar system. It is organized in a hierarchical manner so that every chapter builds upon preceding ones. Starting with historical perspectives on space exploration and the development of the scientific method, the book leads the reader through the solar system. Coverage explains that the origin and subsequent evolution of planets and their satellites can be explained by applications of certain basic principles of physics, chemistry, and celestial mechanics and that surface features of the solid bodies can be interpreted by principles of geology.

Download or read book Advances in Machine Learning and Data Mining for Astronomy written by Michael J. Way and published by CRC Press. This book was released on 2012-03-29 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

Download or read book Advances in Subsurface Data Analytics written by Shuvajit Bhattacharya and published by Elsevier. This book was released on 2022-05-18 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world Offers an analysis of future trends in machine learning in geosciences

Download or read book Clouds and Climate written by A. Pier Siebesma and published by Cambridge University Press. This book was released on 2020-08-20 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive overview of research on clouds and their role in our present and future climate, for advanced students and researchers.