Machine Learning And Data Mining Approaches To Climate Science

Machine Learning And Data Mining Approaches To Climate Science Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Machine Learning And Data Mining Approaches To Climate Science book. This book definitely worth reading, it is an incredibly well-written.

Machine Learning and Data Mining Approaches to Climate Science

Valliappa Lakshmanan,Eric Gilleland,Amy McGovern,Martin Tingley
Machine Learning and Data Mining Approaches to Climate Science Book PDF

Author : Valliappa Lakshmanan,Eric Gilleland,Amy McGovern,Martin Tingley
Publisher : Springer
Page : 252 pages
File Size : 53,6 Mb
Release : 2015-06-30
Category : Science
ISBN : 9783319172200

Get Book

Machine Learning and Data Mining Approaches to Climate Science by Valliappa Lakshmanan,Eric Gilleland,Amy McGovern,Martin Tingley Pdf

This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is headed. Given its interdisciplinary nature, Climate Informatics offers insights, tools and methods that are increasingly needed in order to understand the climate system, an aspect which in turn has become crucial because of the threat of climate change. There has been a veritable explosion in the amount of data produced by satellites, environmental sensors and climate models that monitor, measure and forecast the earth system. In order to meaningfully pursue knowledge discovery on the basis of such voluminous and diverse datasets, it is necessary to apply machine learning methods, and Climate Informatics lies at the intersection of machine learning and climate science. This book grew out of the fourth workshop on Climate Informatics held in Boulder, Colorado in Sep. 2014.

Big Data Mining for Climate Change

Zhihua Zhang
Big Data Mining for Climate Change Book PDF

Author : Zhihua Zhang
Publisher : Unknown
Page : 344 pages
File Size : 52,9 Mb
Release : 2019-12
Category : Electronic
ISBN : 9780128187036

Get Book

Big Data Mining for Climate Change by Zhihua Zhang Pdf

Big Data Mining for Climate Change addresses how to manage the vast amount of information available for analysis. Climate change and its environmental, economic and social consequences are widely recognized as the biggest, most interconnected problem facing humanity. There is a huge amount of potential information currently available...and it is growing exponentially. This book walks through the latest research and how to navigate the resources available using big data applications. It is appropriate for scientists and advanced students studying climate change from a number of disciplines, including the atmospheric sciences, oceanic sciences, geography, environment sciences, ecology, energy, economics, engineering and public policy. Provides a step-by-step guide for applying big data mining tools to climate and environmental research Presents a comprehensive review of theory and algorithms of big data mining for climate change Includes current research in climate and environmental science as it relates to using big data algorithms

Patterns Identification and Data Mining in Weather and Climate

Abdelwaheb Hannachi
Patterns Identification and Data Mining in Weather and Climate Book PDF

Author : Abdelwaheb Hannachi
Publisher : Unknown
Page : 0 pages
File Size : 54,7 Mb
Release : 2021
Category : Electronic
ISBN : 3030670740

Get Book

Patterns Identification and Data Mining in Weather and Climate by Abdelwaheb Hannachi Pdf

Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K.

Large-Scale Machine Learning in the Earth Sciences

Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Large Scale Machine Learning in the Earth Sciences Book PDF

Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Publisher : CRC Press
Page : 354 pages
File Size : 51,9 Mb
Release : 2017-08-01
Category : Computers
ISBN : 9781315354460

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.

Big Data Mining for Climate Change

Zhihua Zhang,Jianping Li
Big Data Mining for Climate Change Book PDF

Author : Zhihua Zhang,Jianping Li
Publisher : Elsevier
Page : 346 pages
File Size : 50,7 Mb
Release : 2019-11-20
Category : Science
ISBN : 9780128187043

Get Book

Big Data Mining for Climate Change by Zhihua Zhang,Jianping Li Pdf

Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation’s big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy. Provides a step-by-step guide for applying big data mining tools to climate and environmental research Presents a comprehensive review of theory and algorithms of big data mining for climate change Includes current research in climate and environmental science as it relates to using big data algorithms

Data Mining

Ian H. Witten,Eibe Frank,Mark A. Hall
Data Mining Book PDF

Author : Ian H. Witten,Eibe Frank,Mark A. Hall
Publisher : Elsevier
Page : 665 pages
File Size : 48,6 Mb
Release : 2011-02-03
Category : Computers
ISBN : 9780080890364

Get Book

Data Mining by Ian H. Witten,Eibe Frank,Mark A. Hall Pdf

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Computational Intelligent Data Analysis for Sustainable Development

Ting Yu,Nitesh Chawla,Simeon Simoff
Computational Intelligent Data Analysis for Sustainable Development Book PDF

Author : Ting Yu,Nitesh Chawla,Simeon Simoff
Publisher : CRC Press
Page : 443 pages
File Size : 44,9 Mb
Release : 2016-04-19
Category : Business & Economics
ISBN : 9781439895955

Get Book

Computational Intelligent Data Analysis for Sustainable Development by Ting Yu,Nitesh Chawla,Simeon Simoff Pdf

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present

Patterns Identification and Data Mining in Weather and Climate

Abdelwaheb Hannachi
Patterns Identification and Data Mining in Weather and Climate Book PDF

Author : Abdelwaheb Hannachi
Publisher : Springer Nature
Page : 600 pages
File Size : 48,8 Mb
Release : 2021-05-06
Category : Science
ISBN : 9783030670733

Get Book

Patterns Identification and Data Mining in Weather and Climate by Abdelwaheb Hannachi Pdf

Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K.

Handbook On Big Data And Machine Learning In The Physical Sciences (In 2 Volumes)

Anonim
Handbook On Big Data And Machine Learning In The Physical Sciences In 2 Volumes Book PDF

Author : Anonim
Publisher : World Scientific
Page : 1001 pages
File Size : 43,8 Mb
Release : 2020-03-10
Category : Computers
ISBN : 9789811204586

Get Book

Handbook On Big Data And Machine Learning In The Physical Sciences (In 2 Volumes) by Anonim Pdf

This compendium provides a comprehensive collection of the emergent applications of big data, machine learning, and artificial intelligence technologies to present day physical sciences ranging from materials theory and imaging to predictive synthesis and automated research. This area of research is among the most rapidly developing in the last several years in areas spanning materials science, chemistry, and condensed matter physics.Written by world renowned researchers, the compilation of two authoritative volumes provides a distinct summary of the modern advances in instrument — driven data generation and analytics, establishing the links between the big data and predictive theories, and outlining the emerging field of data and physics-driven predictive and autonomous systems.

Statistical and Machine-Learning Data Mining

Bruce Ratner
Statistical and Machine Learning Data Mining Book PDF

Author : Bruce Ratner
Publisher : CRC Press
Page : 544 pages
File Size : 47,6 Mb
Release : 2012-02-28
Category : Business & Economics
ISBN : 9781466551213

Get Book

Statistical and Machine-Learning Data Mining by Bruce Ratner Pdf

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Data Mining for Geoinformatics

Guido Cervone,Jessica Lin,Nigel Waters
Data Mining for Geoinformatics Book PDF

Author : Guido Cervone,Jessica Lin,Nigel Waters
Publisher : Springer Science & Business Media
Page : 166 pages
File Size : 52,8 Mb
Release : 2013-08-16
Category : Computers
ISBN : 9781461476696

Get Book

Data Mining for Geoinformatics by Guido Cervone,Jessica Lin,Nigel Waters Pdf

The rate at which geospatial data is being generated exceeds our computational capabilities to extract patterns for the understanding of a dynamically changing world. Geoinformatics and data mining focuses on the development and implementation of computational algorithms to solve these problems. This unique volume contains a collection of chapters on state-of-the-art data mining techniques applied to geoinformatic problems of high complexity and important societal value. Data Mining for Geoinformatics addresses current concerns and developments relating to spatio-temporal data mining issues in remotely-sensed data, problems in meteorological data such as tornado formation, estimation of radiation from the Fukushima nuclear power plant, simulations of traffic data using OpenStreetMap, real time traffic applications of data stream mining, visual analytics of traffic and weather data and the exploratory visualization of collective, mobile objects such as the flocking behavior of wild chickens. This book is designed for researchers and advanced-level students focused on computer science, earth science and geography as a reference or secondary text book. Practitioners working in the areas of data mining and geoscience will also find this book to be a valuable reference.

Introduction to Environmental Data Science

William W. Hsieh
Introduction to Environmental Data Science Book PDF

Author : William W. Hsieh
Publisher : Cambridge University Press
Page : 650 pages
File Size : 52,7 Mb
Release : 2022-12-31
Category : Science
ISBN : 9781009301800

Get Book

Introduction to Environmental Data Science by William W. Hsieh Pdf

Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography, pattern recognition for satellite images from remote sensing, management of agriculture and forests, assessment of climate change, and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills and online data sets allow readers to practise analysis of real data.

Machine Learning for Ecology and Sustainable Natural Resource Management

Grant Humphries,Dawn R. Magness,Falk Huettmann
Machine Learning for Ecology and Sustainable Natural Resource Management Book PDF

Author : Grant Humphries,Dawn R. Magness,Falk Huettmann
Publisher : Springer
Page : 441 pages
File Size : 50,7 Mb
Release : 2018-11-05
Category : Science
ISBN : 9783319969787

Get Book

Machine Learning for Ecology and Sustainable Natural Resource Management by Grant Humphries,Dawn R. Magness,Falk Huettmann Pdf

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence

Arun Lal Srivastav,Ashutosh Kumar Dubey,Abhishek Kumar,Sushil Kumar Narang,Moonis Ali Khan
Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence Book PDF

Author : Arun Lal Srivastav,Ashutosh Kumar Dubey,Abhishek Kumar,Sushil Kumar Narang,Moonis Ali Khan
Publisher : Elsevier
Page : 500 pages
File Size : 55,5 Mb
Release : 2022-11-11
Category : Science
ISBN : 9780323997157

Get Book

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence by Arun Lal Srivastav,Ashutosh Kumar Dubey,Abhishek Kumar,Sushil Kumar Narang,Moonis Ali Khan Pdf

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technologies as related to the impacts of climate change and its potential to prevent/remediate the effects. As such, different types of algorithms, mathematical relations and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact, chances of improving and saving lives and creating a healthier world. This book covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e., climate action. Includes case studies on the application of AI and machine learning for monitoring climate change effects and management Features applications of software and algorithms for modeling and forecasting climate change Shows how real-time monitoring of specific factors (temperature, level of greenhouse gases, rain fall patterns, etc.) are responsible for climate change and possible mitigation efforts to achieve environmental sustainability

Downscaling Techniques for High-Resolution Climate Projections

Rao Kotamarthi,Katharine Hayhoe,Donald Wuebbles,Linda O. Mearns,Jennifer Jacobs,Jennifer Jurado
Downscaling Techniques for High Resolution Climate Projections Book PDF

Author : Rao Kotamarthi,Katharine Hayhoe,Donald Wuebbles,Linda O. Mearns,Jennifer Jacobs,Jennifer Jurado
Publisher : Cambridge University Press
Page : 213 pages
File Size : 54,6 Mb
Release : 2021-02-11
Category : Nature
ISBN : 9781108473750

Get Book

Downscaling Techniques for High-Resolution Climate Projections by Rao Kotamarthi,Katharine Hayhoe,Donald Wuebbles,Linda O. Mearns,Jennifer Jacobs,Jennifer Jurado Pdf

A practical guide to understanding, using and producing downscaled climate data, for researchers, graduate students, policy makers and practitioners.