Big data mining for climate change

Table of Contents

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

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.

https://www.elsevier.com/books/big-data-mining-for-climate-change/zhang/978-0-12-818703-6