Machine Learning for Planetary Science

Machine Learning for Planetary Science
Author: Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publsiher: Elsevier
Total Pages: 400
Release: 2021-12-01
ISBN 10: 0128187220
ISBN 13: 9780128187227
Language: EN, FR, DE, ES & NL

Machine Learning for Planetary Science Book Review:

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

Large Scale Machine Learning in the Earth Sciences

Large Scale Machine Learning in the Earth Sciences
Author: Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Publsiher: CRC Press
Total Pages: 208
Release: 2017-08-01
ISBN 10: 1315354462
ISBN 13: 9781315354460
Language: EN, FR, DE, ES & NL

Large Scale Machine Learning in the Earth Sciences Book Review:

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.

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
Author: Enrico Camporeale,Simon Wing,Jay Johnson
Publsiher: Elsevier
Total Pages: 454
Release: 2018-05-31
ISBN 10: 0128117893
ISBN 13: 9780128117897
Language: EN, FR, DE, ES & NL

Machine Learning Techniques for Space Weather Book Review:

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

Introduction to Python in Earth Science Data Analysis

Introduction to Python in Earth Science Data Analysis
Author: Maurizio Petrelli
Publsiher: Springer Nature
Total Pages: 229
Release: 2021-09-16
ISBN 10: 3030780554
ISBN 13: 9783030780555
Language: EN, FR, DE, ES & NL

Introduction to Python in Earth Science Data Analysis Book Review:

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.

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
Author: Anonim
Publsiher: Academic Press
Total Pages: 316
Release: 2020-09-25
ISBN 10: 0128216840
ISBN 13: 9780128216842
Language: EN, FR, DE, ES & NL

Machine Learning and Artificial Intelligence in Geosciences Book Review:

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

Deep Learning for the Earth Sciences

Deep Learning for the Earth Sciences
Author: Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2021-08-16
ISBN 10: 1119646146
ISBN 13: 9781119646143
Language: EN, FR, DE, ES & NL

Deep Learning for the Earth Sciences Book Review:

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.

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
Author: Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publsiher: CRC Press
Total Pages: 744
Release: 2012-03-29
ISBN 10: 1439841748
ISBN 13: 9781439841747
Language: EN, FR, DE, ES & NL

Advances in Machine Learning and Data Mining for Astronomy Book Review:

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

Machine Learning Methods in the Environmental Sciences

Machine Learning Methods in the Environmental Sciences
Author: William W. Hsieh
Publsiher: Cambridge University Press
Total Pages: 349
Release: 2009-07-30
ISBN 10: 0521791928
ISBN 13: 9780521791922
Language: EN, FR, DE, ES & NL

Machine Learning Methods in the Environmental Sciences Book Review:

A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
Author: Anonim
Publsiher: Academic Press
Total Pages: 232
Release: 2020-10-19
ISBN 10: 0128216697
ISBN 13: 9780128216699
Language: EN, FR, DE, ES & NL

Machine Learning and Artificial Intelligence in Geosciences Book Review:

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

Earth Observation Open Science and Innovation

Earth Observation Open Science and Innovation
Author: Pierre-Philippe Mathieu,Christoph Aubrecht
Publsiher: Springer
Total Pages: 330
Release: 2018-01-23
ISBN 10: 3319656333
ISBN 13: 9783319656335
Language: EN, FR, DE, ES & NL

Earth Observation Open Science and Innovation Book Review:

This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Author: Petr Skoda,Fathalrahman Adam
Publsiher: Elsevier
Total Pages: 472
Release: 2020-04-10
ISBN 10: 0128191554
ISBN 13: 9780128191552
Language: EN, FR, DE, ES & NL

Knowledge Discovery in Big Data from Astronomy and Earth Observation Book Review:

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

New Advances in Machine Learning

New Advances in Machine Learning
Author: Yagang Zhang
Publsiher: BoD – Books on Demand
Total Pages: 374
Release: 2010-02-01
ISBN 10: 953307034X
ISBN 13: 9789533070346
Language: EN, FR, DE, ES & NL

New Advances in Machine Learning Book Review:

The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call “learning” tasks, as we use the word in daily life. It is also broad enough to encompass computers that improve from experience in quite straightforward ways. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners. The wide scope of the book provides a good introduction to many approaches of machine learning, and it is also the source of useful bibliographical information.

Large Scale Machine Learning in the Earth Sciences

Large Scale Machine Learning in the Earth Sciences
Author: Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Publsiher: CRC Press
Total Pages: 208
Release: 2017-08-01
ISBN 10: 1498703887
ISBN 13: 9781498703888
Language: EN, FR, DE, ES & NL

Large Scale Machine Learning in the Earth Sciences Book Review:

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.

Large Meteorite Impacts and Planetary Evolution VI

Large Meteorite Impacts and Planetary Evolution VI
Author: Wolf Uwe Reimold,Christian Koeberl
Publsiher: Geological Society of America
Total Pages: 135
Release: 2021-09-23
ISBN 10: 081372550X
ISBN 13: 9780813725505
Language: EN, FR, DE, ES & NL

Large Meteorite Impacts and Planetary Evolution VI Book Review:

"This volume contains a sizable suite of contributions dealing with regional impact records (Australia, Sweden), impact craters and impactites, early Archean impacts and geophysical characteristics of impact structures, shock metamorphic investigations, post-impact hydrothermalism, and structural geology and morphometry of impact structures - on Earth and Mars"--

Machine Learning

Machine Learning
Author: Peter Flach
Publsiher: Cambridge University Press
Total Pages: 396
Release: 2012-09-20
ISBN 10: 1107096391
ISBN 13: 9781107096394
Language: EN, FR, DE, ES & NL

Machine Learning Book Review:

Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

Machine Learning for Decision Makers

Machine Learning for Decision Makers
Author: Patanjali Kashyap
Publsiher: Apress
Total Pages: 355
Release: 2018-01-04
ISBN 10: 1484229886
ISBN 13: 9781484229880
Language: EN, FR, DE, ES & NL

Machine Learning for Decision Makers Book Review:

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Overcoming Data Scarcity in Earth Science

Overcoming Data Scarcity in Earth Science
Author: Angela Gorgoglione,Alberto Castro Casales,Christian Chreties Ceriani,Lorena Etcheverry Venturini
Publsiher: MDPI
Total Pages: 94
Release: 2020-05-22
ISBN 10: 3039282107
ISBN 13: 9783039282104
Language: EN, FR, DE, ES & NL

Overcoming Data Scarcity in Earth Science Book Review:

heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin Brazil

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin  Brazil
Author: Elezhan Zhakiya
Publsiher: Unknown
Total Pages: 28
Release: 2016
ISBN 10: 1928374650XXX
ISBN 13: OCLC:1031985504
Language: EN, FR, DE, ES & NL

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin Brazil Book Review:

Machine Learning techniques are being widely used in Social Sciences to find connections amongst various variables. Machine Learning connects features across different fields that do not seem to have known mathematical relationships with each other. In natural resource prospecting, machine learning can be applied to connect geochemical, geophysical, and geological variables. However, the biggest challenge in machine learning remains obtaining the data to train the ML algorithms. Here, we have applied machine learning on data extracted from maps via image processing. While the overall accuracy of prediction remains as low as 33% at this stage, we see places where the algorithm can be improved and the accuracy increased.

Machine Learning

Machine Learning
Author: Yagang Zhang
Publsiher: BoD – Books on Demand
Total Pages: 446
Release: 2010-02-01
ISBN 10: 9533070331
ISBN 13: 9789533070339
Language: EN, FR, DE, ES & NL

Machine Learning Book Review:

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking machines and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, and they mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS). Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT and RNA target prediction. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners.

Machine Learning Optimization and Data Science

Machine Learning  Optimization  and Data Science
Author: Giuseppe Nicosia,Varun Ojha,Emanuele La Malfa,Giorgio Jansen,Vincenzo Sciacca,Panos M. Pardalos,Giovanni Giuffrida,Renato Umeton
Publsiher: Springer Nature
Total Pages: 776
Release: 2020
ISBN 10: 3030645800
ISBN 13: 9783030645809
Language: EN, FR, DE, ES & NL

Machine Learning Optimization and Data Science Book Review:

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.