Machine Learning For Planetary Science

Author : Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publisher : Elsevier
Page : 234 pages
File Size : 41,6 Mb
Release : 2022-03-22
Category : Science
ISBN : 9780128187227

Get Book

Machine Learning for Planetary Science by Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner Pdf

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

Author : Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publisher : John Wiley & Sons
Page : 436 pages
File Size : 55,6 Mb
Release : 2021-08-16
Category : Technology & Engineering
ISBN : 9781119646143

Get Book

Deep Learning for the Earth Sciences by Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein Pdf

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.

Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Publisher : CRC Press
Page : 238 pages
File Size : 46,5 Mb
Release : 2017-08-01
Category : Computers
ISBN : 9781498703888

Get Book

Large-Scale Machine Learning in the Earth Sciences by Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser Pdf

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.

Author : Hossein Bonakdari,Isa Ebtehaj,Joseph D. Ladouceur
Publisher : Elsevier
Page : 390 pages
File Size : 54,9 Mb
Release : 2023-07-03
Category : Science
ISBN : 9780443152856

Get Book

Machine Learning in Earth, Environmental and Planetary Sciences by Hossein Bonakdari,Isa Ebtehaj,Joseph D. Ladouceur Pdf

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

Author : Enrico Camporeale,Simon Wing,Jay Johnson
Publisher : Elsevier
Page : 454 pages
File Size : 51,8 Mb
Release : 2018-05-31
Category : Science
ISBN : 9780128117897

Get Book

Machine Learning Techniques for Space Weather by Enrico Camporeale,Simon Wing,Jay Johnson Pdf

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

Author : Thomas Berger,Enrico Camporeale,Bala Poduval,Veronique A. Delouille,Sophie A. Murray
Publisher : Frontiers Media SA
Page : 240 pages
File Size : 55,9 Mb
Release : 2021-11-24
Category : Science
ISBN : 9782889716715

Get Book

Machine Learning in Heliophysics by Thomas Berger,Enrico Camporeale,Bala Poduval,Veronique A. Delouille,Sophie A. Murray Pdf

Author : Anonim
Publisher : Academic Press
Page : 318 pages
File Size : 50,6 Mb
Release : 2020-09-22
Category : Science
ISBN : 9780128216842

Get Book

Machine Learning and Artificial Intelligence in Geosciences by Anonim Pdf

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

Author : Hossein Bonakdari,Silvio José Gumiere
Publisher : Elsevier
Page : 0 pages
File Size : 40,7 Mb
Release : 2024-08-01
Category : Science
ISBN : 9780443132926

Get Book

Intelligence Systems for Earth, Environmental and Planetary Sciences by Hossein Bonakdari,Silvio José Gumiere Pdf

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 parts: Methods, covering the fundamentals of intelligence systems, along with an introduction to the preparation of datasets. Models, covering model development, data assimilation, and techniques in each field. Applications, presenting case studies of artificial intelligence and data science solutions to Earth, environmental and planetary sciences problems, as well as future perspectives. This book will be of interest to students, academics and post-graduate 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

Author : Bala Poduval,Karly Pitman,Olga Verkhoglyadova,Peter Wintoft
Publisher : Frontiers Media SA
Page : 203 pages
File Size : 43,9 Mb
Release : 2023-04-12
Category : Science
ISBN : 9782832520581

Get Book

Applications of statistical methods and machine learning in the space sciences by Bala Poduval,Karly Pitman,Olga Verkhoglyadova,Peter Wintoft Pdf

Author : Maurizio Petrelli
Publisher : Springer Nature
Page : 229 pages
File Size : 54,5 Mb
Release : 2021-09-16
Category : Science
ISBN : 9783030780555

Get Book

Introduction to Python in Earth Science Data Analysis by Maurizio Petrelli Pdf

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.

Author : National Academies Of Sciences Engineeri,National Academies of Sciences Engineering and Medicine,Division On Engineering And Physical Sci,Division On Earth And Life Studies,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Ocean Studies Board,Board on Earth Sciences and Resources,Board on Atmospheric Sciences and Climate
Publisher : National Academies Press
Page : 128 pages
File Size : 48,5 Mb
Release : 2022-07-13
Category : Computers
ISBN : 0309688531

Get Book

Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop by National Academies Of Sciences Engineeri,National Academies of Sciences Engineering and Medicine,Division On Engineering And Physical Sci,Division On Earth And Life Studies,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Ocean Studies Board,Board on Earth Sciences and Resources,Board on Atmospheric Sciences and Climate Pdf

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.

Author : Maurizio Petrelli
Publisher : Springer Nature
Page : 214 pages
File Size : 52,7 Mb
Release : 2023-09-22
Category : Science
ISBN : 9783031351143

Get Book

Machine Learning for Earth Sciences by Maurizio Petrelli Pdf

This textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. In detail, it starts by describing the basics of machine learning and its potentials in Earth Sciences to solve geological problems. It describes the main Python tools devoted to ML, the typival workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work. The book provides many examples of ML application to Earth Sciences problems in many fields, such as the clustering and dimensionality reduction in petro-volcanological studies, the clustering of multi-spectral data, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals.

Author : Ziheng Sun,Nicoleta Cristea,Pablo Rivas
Publisher : Elsevier
Page : 430 pages
File Size : 43,6 Mb
Release : 2023-04-27
Category : Science
ISBN : 9780323972161

Get Book

Artificial Intelligence in Earth Science by Ziheng Sun,Nicoleta Cristea,Pablo Rivas Pdf

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

Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publisher : CRC Press
Page : 744 pages
File Size : 49,7 Mb
Release : 2012-03-29
Category : Computers
ISBN : 9781439841747

Get Book

Advances in Machine Learning and Data Mining for Astronomy by Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava Pdf

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

Author : Patrick Bangert
Publisher : Elsevier
Page : 276 pages
File Size : 55,6 Mb
Release : 2021-01-14
Category : Technology & Engineering
ISBN : 9780128226001

Get Book

Machine Learning and Data Science in the Power Generation Industry by Patrick Bangert Pdf

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