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.
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.
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
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.
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