Big Data Mining for Climate Change addresses one of the fundamental issues facing researchers of climate or the environment; how to manage the vast amount of information available and analyse it. 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 available currently, and it is growing exponentially. However, the issue is so complex and interconnected that, it is difficult to analyse using classic data analysis methods. These traditional methods have proven insufficient because of the size, scope, diversity and dynamic nature of the data. Recently, new integrated and interdisciplinary big data mining approaches have been 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 walks through the latest research and how to navigate the resources available using big data applications. This book is appropriate for scientists and advanced students studying climate change from a number of disciplines including atmospheric sciences, oceanic sciences, geography, environment sciences, ecology, energy, economics, engineering, and public policy.
- A step by step guide for applying big data mining tools to climate and environmental research
- 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. Randon 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
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- © Elsevier 2020
- Paperback ISBN:
Zhihua Zhang is a Tenured Research Professor & Senior Scientist and an associate director of Polar Climate and Environment Key Laboratory in Beijing Normal University, China. His research interests are Mechanisms of Climate Change, Climatic Time Series Analysis, Signal & Image Processing, and Applied Mathematics. Recently, he has published more than 40 first-authored papers and is editoring a special volume on carbon emissions reduction for the Journal of Cleaner Production as the managing guest editor. Zhihua Zhang is an Editor-in-Chief of “American Journal of Climate Change”, an associate editor of “Arabian Journal of Geosciences”, an Editorial Board Member of “Advances in Meteorology”, an Associate Editor of “EURASIP Journal on Advances in Signal Processing”, an Editorial Board Member of “International Journal of Global Warming”, and an Editorial Board Member of “Journal of Applied Mathematics”. Zhihua Zhang’s research is supported by National Key Science Program for Global Change Research, Fundamental Research Funds for the Central Universities (Key Program) and NSFC.
Tenured Research Professor and Senior Scientist, Beijing Normal University, China
Jianping Li, Ph.D, a full professor at Beijing Normal University, Vice-Chair of the IUGG Union Commission on Climatic and Environmental Change (CCEC), Executive Secretary-General of the International Commission of Climate (ICCL )/ IAMAS , Fel low of IUGG , Fellow of Royal Meteorological Society (RMetS), an Affiliate Faculty of University of Hawaii, and Editor of a number of Climate journals. His major research interests include climate dynamics and climate change, monsoon and annular modes. He has published more than 300 peer-reviewed papers and has edited several books as well.
Full Professor, Beijing Normal University