Big Data Mining for Climate Change
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
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Big Data Mining for Climate Change
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
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Big Data Mining for Climate Change

Big Data Mining for Climate Change

Big Data Mining for Climate Change
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Big Data Mining for Climate Change

Big Data Mining for Climate Change

Overview

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

Product Details

ISBN-13: 9780128187043
Publisher: Elsevier Science
Publication date: 11/20/2019
Sold by: Barnes & Noble
Format: eBook
Pages: 344
File size: 13 MB
Note: This product may take a few minutes to download.

About the Author

Prof. Zhang's long-standing researches focus on big earth data, climate change mechanisms, ocean dynamics, environmental evolution and sustainability. Prof Zhang has published six books as first author: Ø Frame Theory in Data Science (Springer, 2024), Ø Environmental Data Analysis (DeGruyter, 2nd Edition, 2023), Ø Big Data Mining for Climate Change (Elsevier, 2020), Ø Patterns and Mechanisms of Climate, Paleoclimate and Paleoenvironmental Change from Low-Latitude Regions (Springer, 2019), Ø Multivariate Time Series Analysis in Climate & Environmental Research (Springer, 2018), Ø Mathematical and Physical Fundamentals of Climate Change (Elsevier, 2015) Prof. Zhang has published more than 80 articles, highlighting many times by New Scientist (UK), China Science Daily, and China Social Science Daily. Currently, Prof. Zhang is serving as an Editor-in-Chief of Int J Big Data Mining for Global Warming (World Scientific); an Associate Editor of Environ Dev Sustain (Springer), EURASIP J Adv Signal Process (Springer), and Int J Climate Change Strat & Manag (Emerald); and an Editorial Board Member of Earth Sci Informatics (Springer), PLoS ONE, Open Geosci (DeGruyter), Int J Global Warming (Indersci). Prof. Zhang is serving as the first track chair of Mediterranean Geosciences Union Annual Meeting (2021-now), and was invited as a plenary/keynote speaker at 2023 Mediterranean Geosciences Union Annual Meeting (Turkey) and 2024 International Conference on Intelligent Information Processing (Romania)Jianping Li, PhD, a full professor at Ocean University of China, Chair of the IUGG Union Commission on Climatic and Environmental Change (CCEC), President of the International Commission of Climate (ICCL)/IAMAS, Fellow of IUGG, Fellow of Royal Meteorological Society, an Affiliate Faculty of University of Hawaii, Executive Editor of Climate Dynamics, and Editor of a number of known climate journals. His major research interests include climate dynamics and climate change, monsoon, air-sea interaction and annular modes. He has published more than 400 peer-reviewed papers, and has edited several books.
Prof. Zhang’s long-standing researches focus on big earth data, climate change mechanisms, ocean dynamics, environmental evolution and sustainability. Prof Zhang has published six books as first author: Ø Frame Theory in Data Science (Springer, 2024), Ø Environmental Data Analysis (DeGruyter, 2nd Edition, 2023), Ø Big Data Mining for Climate Change (Elsevier, 2020), Ø Patterns and Mechanisms of Climate, Paleoclimate and Paleoenvironmental Change from Low-Latitude Regions (Springer, 2019), Ø Multivariate Time Series Analysis in Climate & Environmental Research (Springer, 2018), Ø Mathematical and Physical Fundamentals of Climate Change (Elsevier, 2015) Prof. Zhang has published more than 80 articles, highlighting many times by New Scientist (UK), China Science Daily, and China Social Science Daily. Currently, Prof. Zhang is serving as an Editor-in-Chief of Int J Big Data Mining for Global Warming (World Scientific); an Associate Editor of Environ Dev Sustain (Springer), EURASIP J Adv Signal Process (Springer), and Int J Climate Change Strat & Manag (Emerald); and an Editorial Board Member of Earth Sci Informatics (Springer), PLoS ONE, Open Geosci (DeGruyter), Int J Global Warming (Indersci). Prof. Zhang is serving as the first track chair of Mediterranean Geosciences Union Annual Meeting (2021-now), and was invited as a plenary/keynote speaker at 2023 Mediterranean Geosciences Union Annual Meeting (Turkey) and 2024 International Conference on Intelligent Information Processing (Romania)

Table of Contents

1. Big Datasets and Platforms for Climate Change2. Feature Extraction of Big Climate Data3. Deep learning for Climate Patterns4. Climate Networks5. Random Networks and Climate Entropy6. Spectra of Climate Networks7. Simulations of Climate Systems8. Dimension reduction9. Big Data Analysis for Carbon Footprint10. Big Data Driven Low Carbon Management

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A comprehensive work on the theory, models, algorithms and platforms involved in big data mining for climate change and related fields

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