Machine Learning in Earth Environmental and Planetary Sciences

Machine Learning in Earth  Environmental and Planetary Sciences
Author: Hossein Bonakdari,Isa Ebtehaj,Joseph D. Ladouceur
Publsiher: Elsevier
Total Pages: 390
Release: 2023-07-03
Genre: Science
ISBN: 9780443152856

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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

Intelligence Systems for Earth Environmental and Planetary Sciences

Intelligence Systems for Earth  Environmental and Planetary Sciences
Author: Hossein Bonakdari,Silvio José Gumiere
Publsiher: Elsevier
Total Pages: 0
Release: 2024-08-01
Genre: Science
ISBN: 0443132933

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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. Intelligence systems for Earth, Environmental and Planetary Sciences 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.

Machine Learning for Planetary Science

Machine Learning for Planetary Science
Author: Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publsiher: Elsevier
Total Pages: 234
Release: 2022-03-22
Genre: Science
ISBN: 9780128187227

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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
Genre: Computers
ISBN: 9781498703888

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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.

Computers in Earth and Environmental Sciences

Computers in Earth and Environmental Sciences
Author: Hamid Reza Pourghasemi
Publsiher: Elsevier
Total Pages: 702
Release: 2021-09-22
Genre: Computers
ISBN: 9780323898614

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Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management addresses the need for a comprehensive book that focuses on multi-hazard assessments, natural and manmade hazards, and risk management using new methods and technologies that employ GIS, artificial intelligence, spatial modeling, machine learning tools and meta-heuristic techniques. The book is clearly organized into four parts that cover natural hazards, environmental hazards, advanced tools and technologies in risk management, and future challenges in computer applications to hazards and risk management. Researchers and professionals in Earth and Environmental Science who require the latest technologies and advances in hazards, remote sensing, geosciences, spatial modeling and machine learning will find this book to be an invaluable source of information on the latest tools and technologies available. Covers advanced tools and technologies in risk management of hazards in both the Earth and Environmental Sciences Details the benefits and applications of various technologies to assist researchers in choosing the most appropriate techniques for purpose Expansively covers specific future challenges in the use of computers in Earth and Environmental Science Includes case studies that detail the applications of the discussed technologies down to individual hazards

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: 436
Release: 2021-08-18
Genre: Technology & Engineering
ISBN: 9781119646167

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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.

Computational Intelligence Techniques in Earth and Environmental Sciences

Computational Intelligence Techniques in Earth and Environmental Sciences
Author: Tanvir Islam,Prashant K. Srivastava,Manika Gupta,Xuan Zhu,Saumitra Mukherjee
Publsiher: Springer Science & Business Media
Total Pages: 266
Release: 2014-02-14
Genre: Science
ISBN: 9789401786423

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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.

Machine Learning Methods in the Environmental Sciences

Machine Learning Methods in the Environmental Sciences
Author: William W. Hsieh
Publsiher: Cambridge University Press
Total Pages: 364
Release: 2009-07-30
Genre: Computers
ISBN: 9780521791922

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