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
[7] B. Crost, C.P. Traeger, Optimal climate policy: uncertainty versus Monte Carlo, Econ. Lett. 120 (2013) 552–558. [8] M. Gao, L.T. Yin, ... [22] Z. Zhang, Mathematical and Physical Fundamentals of Climate Change, Elsevier, 2015.
This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets ...
Johnson and Nicholas, 'Male and Female Living Standards in England and Wales, 1812–1867', 470–81; Robert J. Barro, 'Democracy and Growth', Journal of Economic Growth 1 (1996), 1–27; Jakob B. Madsen, James B. Ang, and Rajabrata Banerjee, ...
Amity University London Campus will be conducting International Conference on Intelligent Engineering and Management We will like to bring together the scholars, scientists and industrialists from all across the world to the wide spectrum ...
This work considers both: it enables mining complex data from high impact applications, such as breast cancer diagnosis, region classification in satellite images, assistance to climate change forecast, recommendation systems for the Web ...
This book informs the future users of climate models and the decision-makers of tomorrow by providing the depth they need.
This edited book provides techniques which address various aspects of big data collection and analysis from social media platforms and beyond.
F. Olaiya and A. B. Adeyemo, “Application of data mining techniques in weather prediction and climate change studies,” Int J Inform Eng Electr Bus (IJIEEB), 4(1), 51, 2012. 43. Z. Chen, Y. Xie, Y. Cheng, K. Zhang, A. Agrawal, ...
"This book examines current, state-of-the-art research in the areas of data science, machine learning, data mining, optimization, artificial intelligence, statistics, and the interactions, linkages, and applications of knowledge-based ...
Merely observing these events unfold – the growth of climate data, a wide-range of challenging realworld research questions, and the emergence of data mining and machine learning in virtually every domain where data are reasonably ...