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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
Scientists and advanced students in atmospheric sciences, oceanic sciences, geography, environment sciences, ecology, energy, economics, engineering, and public policy
1. Big Datasets and Platforms for Climate Change
2. Feature Extraction of Big Climate Data
3. Deep learning for Climate Patterns
4. Climate Networks
5. Random Networks and Climate Entropy
6. Spectra of Climate Networks
7. Simulations of Climate Systems
8. Dimension reduction
9. Big Data Analysis for Carbon Footprint
10. Big Data Driven Low Carbon Management
- No. of pages:
- © Elsevier 2020
- 20th November 2019
- Paperback ISBN:
- eBook ISBN:
Zhihua Zhang is a Taishan distinguished professor and director of climate modeling laboratory in Shandong University, China. His research interests are Mechanisms of Climate Change, Big Data Mining, Carbon Emissions, Climate Policy and Sustainability. Prof. Zhang has published 4 first-authored books and about 50 first-authored papers. He is a Chief Editor, Associate Editor, or Editorial Board Member in several global and regional known journals on Climate Change, Meteorology and Environmental Data.
Taishan Distinguished Professor, Shandong University, China
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.
Full Professor, Ocean University of China
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