Computational Statistics In The Earth Sciences
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Computational Statistics In The Earth Sciences
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Author : Alan D. Chave
language : en
Publisher: Cambridge University Press
Release Date : 2017-10-19
Computational Statistics In The Earth Sciences written by Alan D. Chave and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-19 with Mathematics categories.
This book combines theoretical underpinnings of statistics with practical analysis of Earth sciences data using MATLAB. Supplementary resources are available online.
Computational Intelligence Techniques In Earth And Environmental Sciences
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Author : Tanvir Islam
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-02-14
Computational Intelligence Techniques In Earth And Environmental Sciences written by Tanvir Islam and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-14 with Science categories.
Computational intelligence techniques have enjoyed growing interest in recent decades among the earth and environmental science research communities for their powerful ability to solve and understand various complex problems and develop novel approaches toward a sustainable earth. This book compiles a collection of recent developments and rigorous applications of computational intelligence in these disciplines. Techniques covered include artificial neural networks, support vector machines, fuzzy logic, decision-making algorithms, supervised and unsupervised classification algorithms, probabilistic computing, hybrid methods and morphic computing. Further topics given treatment in this volume include remote sensing, meteorology, atmospheric and oceanic modeling, climate change, environmental engineering and management, catastrophic natural hazards, air and environmental pollution and water quality. By linking computational intelligence techniques with earth and environmental science oriented problems, this book promotes synergistic activities among scientists and technicians working in areas such as data mining and machine learning. We believe that a diverse group of academics, scientists, environmentalists, meteorologists and computing experts with a common interest in computational intelligence techniques within the earth and environmental sciences will find this book to be of great value.
Statistics Of Earth Science Data
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Author : Graham J. Borradaile
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-05-14
Statistics Of Earth Science Data written by Graham J. Borradaile and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-05-14 with Mathematics categories.
From the reviews: "All in all, Graham Borradaile has written and interesting and idiosyncratic book on statistics for geoscientists that will be welcome among students, researchers, and practitioners dealing with orientation data. That should include engineering geologists who work with things like rock fracture orientation measurements or clast alignment in paleoseismic trenches. It won’t replace the collection of statistics and geostatistics texts in my library, but it will have a place among them and will likely be one of several references to which I turn when working with orientation data.... The text is easy to follow and illustrations are generally clear and easy to read..."(William C. Haneberg, Haneberg Geoscience)
Statistics In The Environmental Earth Sciences
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Author : Andrew T. Walden
language : en
Publisher: Hodder Education
Release Date : 1992
Statistics In The Environmental Earth Sciences written by Andrew T. Walden and has been published by Hodder Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Mathematics categories.
Geodetic Time Series Analysis In Earth Sciences
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Author : Jean-Philippe Montillet
language : en
Publisher: Springer
Release Date : 2019-08-16
Geodetic Time Series Analysis In Earth Sciences written by Jean-Philippe Montillet and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-16 with Science categories.
This book provides an essential appraisal of the recent advances in technologies, mathematical models and computational software used by those working with geodetic data. It explains the latest methods in processing and analyzing geodetic time series data from various space missions (i.e. GNSS, GRACE) and other technologies (i.e. tide gauges), using the most recent mathematical models. The book provides practical examples of how to apply these models to estimate seal level rise as well as rapid and evolving land motion changes due to gravity (ice sheet loss) and earthquakes respectively. It also provides a necessary overview of geodetic software and where to obtain them.
Integrating Data Science And Earth Science
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Author : Laurens M. Bouwer
language : en
Publisher: Springer Nature
Release Date : 2022-07-14
Integrating Data Science And Earth Science written by Laurens M. Bouwer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-14 with Science categories.
This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.
Large Scale Inverse Problems
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Author : Mike Cullen
language : en
Publisher: Walter de Gruyter
Release Date : 2013-08-29
Large Scale Inverse Problems written by Mike Cullen and has been published by Walter de Gruyter this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-29 with Mathematics categories.
This book is thesecond volume of a three volume series recording the "Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment" that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications. The solution of inverse problems is fundamental to a wide variety of applications such as weather forecasting, medical tomography, and oil exploration. Regularisation techniques are needed to ensure solutions of sufficient quality to be useful, and soundly theoretically based. This book addresses the common techniques required for all the applications, and is thus truly interdisciplinary. Thiscollection of surveyarticlesfocusses onthe large inverse problems commonly arising in simulation and forecasting in the earth sciences. For example, operational weather forecasting models have between 107 and 108 degrees of freedom. Even so, these degrees of freedom represent grossly space-time averaged properties of the atmosphere. Accurate forecasts require accurate initial conditions. With recent developments in satellite data, there are between 106 and 107 observations each day. However, while these also represent space-time averaged properties, the averaging implicit in the measurements is quite different from that used in the models. In atmosphere and ocean applications, there is a physically-based model available which can be used to regularise the problem. We assume that there is a set of observations with known error characteristics available over a period of time. The basic deterministic technique is to fit a model trajectory to the observations over a period of time to within the observation error. Since the model is not perfect the model trajectory has to be corrected, which defines the data assimilation problem. The stochastic view can be expressed by using an ensemble of model trajectories, and calculating corrections to both the mean value and the spread which allow the observations to be fitted by each ensemble member. In other areas of earth science, only the structure of the model formulation itself is known and the aim is to use the past observation history to determine the unknown model parameters. The book records the achievements of Workshop2 "Large-Scale Inverse Problems and Applications in the Earth Sciences". Itinvolves experts in the theory of inverse problems together with experts working on both theoretical and practical aspects of the techniques by which large inverse problems arise in the earth sciences.
Methods And Applications Of Statistics In Engineering Quality Control And The Physical Sciences
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Author : Narayanaswamy Balakrishnan
language : en
Publisher: Wiley
Release Date : 2011-04-12
Methods And Applications Of Statistics In Engineering Quality Control And The Physical Sciences written by Narayanaswamy Balakrishnan and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-12 with Mathematics categories.
Inspired by the Encyclopedia of Statistical Sciences, Second Edition (ESS2e), this volume presents a concise, well-rounded focus on the statistical concepts and applications that are essential for understanding gathered data in the fields of engineering, quality control, and the physical sciences. The book successfully upholds the goals of ESS2e by combining both previously-published and newly developed contributions written by over 100 leading academics, researchers, and practitioner in a comprehensive, approachable format. The result is a succinct reference that unveils modern, cutting-edge approaches to acquiring and analyzing data across diverse subject areas within these three disciplines, including operations research, chemistry, physics, the earth sciences, electrical engineering, and quality assurance. In addition, techniques related to survey methodology, computational statistics, and operations research are discussed, where applicable. Topics of coverage include: optimal and stochastic control, artificial intelligence, quantum mechanics, and fractals.
Statistics And Data Analysis In Geology
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Author : John C. Davis
language : en
Publisher: John Wiley & Sons
Release Date : 2011
Statistics And Data Analysis In Geology written by John C. Davis and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Geology categories.
Special Features: · Offers a comprehensive treatment of statistics in geology.· Topics progress from background information to analysis of geological sequences, then maps, and finally multivariate observations.· The book places special emphasis on probability and statistics, including nonparametric statistics, constant-sum data, eigenvalue calculations, analysis of directional data, mapping and geostatistics, fractals, and multivariate analysis.· The text now includes numerous geological data sets that illustrate how specific computational procedures can be applied to problems in the Earth sciences. All data sets are available on the book's companion Web site.· Each chapter now ends with a set of exercises of greater or lesser complexity that the student can address using methods discussed in the chapter.· Provides expanded coverage of elementary probability theory.· The discussion of nonparametric methods has been expanded to address closure effects.· Coverage of eigenvalues and eigenvectors has been revised.· Includes a new section on singular value decomposition and the relationship between R- and Q-mode factor methods in the chapter on multivariate analysis.· The section on contour mapping has been revised to reflect modern practices.· Includes revised coverage of the many varieties of kriging and provides of series of simple demonstrations that illustrate how geostatistical methodologies work.· Includes a discussion of fractals, a promising area of future research.· The section on regression has been expanded to include several variants that have special significance in the Earth sciences.
Large Scale Machine Learning In The Earth Sciences
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Author : Ashok N. Srivastava
language : en
Publisher: CRC Press
Release Date : 2017-08-01
Large Scale Machine Learning In The Earth Sciences written by Ashok N. Srivastava and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-01 with Computers categories.
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.