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Machine Learning For Planetary Science

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Machine Learning for Planetary Science

Machine Learning for Planetary Science Book
Author : Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publisher : Elsevier
Release : 2021-12-01
ISBN : 0128187220
Language : En, Es, Fr & De

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Book Description :

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

Deep Learning for the Earth Sciences

Deep Learning for the Earth Sciences Book
Author : Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publisher : John Wiley & Sons
Release : 2021-08-16
ISBN : 1119646146
Language : En, Es, Fr & De

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Book Description :

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.

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences Book
Author : Anonim
Publisher : Academic Press
Release : 2020-10-19
ISBN : 0128216697
Language : En, Es, Fr & De

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Book Description :

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

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation Book
Author : Petr Skoda,Fathalrahman Adam
Publisher : Elsevier
Release : 2020-04-10
ISBN : 0128191554
Language : En, Es, Fr & De

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Book Description :

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

Planetary Diversity

Planetary Diversity Book
Author : Elizabeth Tasker,Yuka Fujii,Matthieu Laneuville
Publisher : IOP Publishing Limited
Release : 2020
ISBN : 9780750321389
Language : En, Es, Fr & De

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Book Description :

The last 30 years have seen an irrevocable change in the field of planetary science with the discovery of the first planets around stars other than our own Sun. While approximately 20 percent of the exoplanets we have discovered are close in size to the Earth, the similarity of their surface environment to our home world remains unknown. These conditions will be probed in the coming decade by instruments capable of observing the atmospheric and surface composition of rocky exoplanets. The signature we observe will be a complex combination of the planet's geological, chemical and physical processes.This book presents an exploration of the potential diversity of rocky planets through a quantitative study of how planetary processes change as properties deviate from the Earth. Changes in four specific properties are considered : the presence of a magnetic field, the production and loss of internal heat, planetary composition and volatile abundance.

Large Scale Machine Learning in the Earth Sciences

Large Scale Machine Learning in the Earth Sciences Book
Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Publisher : CRC Press
Release : 2017-08-01
ISBN : 1498703887
Language : En, Es, Fr & De

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Book Description :

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.

Introduction to Python in Earth Science Data Analysis

Introduction to Python in Earth Science Data Analysis Book
Author : Maurizio Petrelli
Publisher : Springer Nature
Release : 2021-09-16
ISBN : 3030780554
Language : En, Es, Fr & De

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Book Description :

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.

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin Brazil

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin  Brazil Book
Author : Elezhan Zhakiya
Publisher : Unknown
Release : 2016
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

Large Meteorite Impacts and Planetary Evolution VI

Large Meteorite Impacts and Planetary Evolution VI Book
Author : Wolf Uwe Reimold,Christian Koeberl
Publisher : Geological Society of America
Release : 2021-09-23
ISBN : 081372550X
Language : En, Es, Fr & De

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Book Description :

"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 for Decision Makers

Machine Learning for Decision Makers Book
Author : Patanjali Kashyap
Publisher : Apress
Release : 2018-01-04
ISBN : 1484229886
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Angela Gorgoglione,Alberto Castro Casales,Christian Chreties Ceriani,Lorena Etcheverry Venturini
Publisher : MDPI
Release : 2020-05-22
ISBN : 3039282107
Language : En, Es, Fr & De

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Book Description :

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.

Machine Learning

Machine Learning Book
Author : Yagang Zhang
Publisher : BoD – Books on Demand
Release : 2010-02-01
ISBN : 9533070331
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Giuseppe Nicosia,Varun Ojha,Emanuele La Malfa,Giorgio Jansen,Vincenzo Sciacca,Panos M. Pardalos,Giovanni Giuffrida,Renato Umeton
Publisher : Springer Nature
Release : 2020
ISBN : 3030645800
Language : En, Es, Fr & De

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Book Description :

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.

Intelligent Data Analysis for Real Life Applications Theory and Practice

Intelligent Data Analysis for Real Life Applications  Theory and Practice Book
Author : Magdalena-Benedito, Rafael
Publisher : IGI Global
Release : 2012-06-30
ISBN : 1466618078
Language : En, Es, Fr & De

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Book Description :

With the recent and enormous increase in the amount of available data sets of all kinds, applying effective and efficient techniques for analyzing and extracting information from that data has become a crucial task. Intelligent Data Analysis for Real-Life Applications: Theory and Practice investigates the application of Intelligent Data Analysis (IDA) to these data sets through the design and development of algorithms and techniques to extract knowledge from databases. This pivotal reference explores practical applications of IDA, and it is essential for academic and research libraries as well as students, researchers, and educators in data analysis, application development, and database management.

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy Book
Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publisher : CRC Press
Release : 2012-03-29
ISBN : 1439841748
Language : En, Es, Fr & De

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Book Description :

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

An Astrobiology Strategy for the Search for Life in the Universe

An Astrobiology Strategy for the Search for Life in the Universe Book
Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Space Studies Board,Committee on Astrobiology Science Strategy for the Search for Life in the Universe
Publisher : National Academies Press
Release : 2019-04-20
ISBN : 0309484162
Language : En, Es, Fr & De

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Book Description :

Astrobiology is the study of the origin, evolution, distribution, and future of life in the universe. It is an inherently interdisciplinary field that encompasses astronomy, biology, geology, heliophysics, and planetary science, including complementary laboratory activities and field studies conducted in a wide range of terrestrial environments. Combining inherent scientific interest and public appeal, the search for life in the solar system and beyond provides a scientific rationale for many current and future activities carried out by the National Aeronautics and Science Administration (NASA) and other national and international agencies and organizations. Requested by NASA, this study offers a science strategy for astrobiology that outlines key scientific questions, identifies the most promising research in the field, and indicates the extent to which the mission priorities in existing decadal surveys address the search for life's origin, evolution, distribution, and future in the universe. This report makes recommendations for advancing the research, obtaining the measurements, and realizing NASA's goal to search for signs of life in the universe.

Discovery Science

Discovery Science Book
Author : Nathalie Japkowicz,Stan Matwin
Publisher : Springer
Release : 2015-10-04
ISBN : 3319242822
Language : En, Es, Fr & De

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Book Description :

This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2015, held in banff, AB, Canada in October 2015. The 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefully reviewed and selected from 44 submissions. The combination of recent advances in the development and analysis of methods for discovering scienti c knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scienti c domains, on the one hand, with the algorithmic advances in machine learning theory, on the other hand, makes every instance of this joint event unique and attractive.

Algorithmic Learning Theory

Algorithmic Learning Theory Book
Author : Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles
Publisher : Springer
Release : 2015-10-04
ISBN : 3319244868
Language : En, Es, Fr & De

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Book Description :

This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.

Advances in Earth Science

Advances in Earth Science Book
Author : J. M. T. Thompson
Publisher : Imperial College Press
Release : 2007
ISBN : 1860948715
Language : En, Es, Fr & De

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Book Description :

Advances in Earth Science outlines the latest developments and new research directions currently being made world-wide in the earth sciences. It contains invited and refereed articles by leading younger researchers on their cutting-edge research, but aimed at a general scientific audience. This exciting volume explains how powerful methodologies such as satellite remote sensing and supercomputing simulations are now profoundly changing research in the earth sciences; how the earth system is increasingly being viewed in a holistic way, linking the atmosphere, ocean and solid earth; and how the societal impact of the research in the earth sciences has never been more important. Published by Imperial College Press in collaboration with the Royal Society of London, the book features many articles originating from invited papers published in the Philosophical Transactions of the Royal Society. Eleven of the distinguished contributors hold prestigious Royal Society Research Fellowships.

Computational Discovery of Scientific Knowledge

Computational Discovery of Scientific Knowledge Book
Author : Saso Dzeroski,Ljupco Todorovski
Publisher : Springer
Release : 2007-08-24
ISBN : 3540739203
Language : En, Es, Fr & De

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Book Description :

This survey provides an introduction to computational approaches to the discovery of communicable scientific knowledge and details recent advances. It is partly inspired by the contributions of the International Symposium on Computational Discovery of Communicable Knowledge, held in Stanford, CA, USA in March 2001, a number of additional invited contributions provide coverage of recent research in computational discovery.