From the Foreword:
"While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. 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 to answer questions of urgent societal interest…I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences."
--Vipin Kumar, University of Minnesota
Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science.
Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored.
The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth.
The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.
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|Series:||Chapman & Hall/CRC Data Mining and Knowledge Discovery Series|
|Sold by:||Barnes & Noble|
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About the Author
Ashok N. Srivastava, Ph.D. is the VP of Data and Artificial Intelligence Systems and the Chief Data Scientist at Verizon. He leads a new research and development center in Palo Alto focusing on building products and technologies powered by big data, large-scale machine learning, and analytics. He is an Adjunct Professor at Stanford University in the Electrical Engineering Department and is the Editor-in-Chief of the AIAA Journal of Aerospace Information Systems. Dr. Srivastava is a Fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA).
He is the author of over 100 research articles, has edited 4 books, has 5 patents awarded, and over 30 under file. He has won numerous awards including the IEEE Computer Society Technical Achievement Award for "pioneering contributions to intelligent information systems," the NASA Exceptional Achievement Medal for contributions to state-of-the-art data mining and analysis, the NASA Honor Award for Outstanding Leadership, the NASA Distinguished Performance Award, several NASA Group Achievement Awards, the Distinguished Engineering Alumni Award from UC Boulder, the IBM Golden Circle Award, and the Department of Education Merit Fellowship.
Dr. Ramakrishna Nemani is a senior Earth scientist with the NASA Advanced Supercomputing division at Ames Research Center, California, USA. He leads NASA's efforts in ecological forecasting to understand the impacts of the impending climatic changes on Earth’s ecosystems and in collaborative computing, bringing scientists together with big data and supercomputing to provide insights into how our planet is changing and the forces underlying such changes.
He has published over 190 papers on a variety of topics including remote sensing, global ecology, ecological forecasting, climatology and scientific computing with over 28000 citations. He served on the science teams of several missions including Landsat-8, NPP, EOS/MODIS, ALOS-2 and GCOM-C. He has received numerous awards from NASA including the exceptional scientific achievement medal in 2008, exceptional achievement medal in 2011, outstanding leadership medal in 2012 and eight group achievement awards.
Karsten Steinhaeuser, Ph.D. is a Research Scientist affiliated with the Department of Computer Science & Engineering at the University of Minnesota and a Data Scientist with Progeny Systems Corporation. His research centers around data mining and machine learning, in particular construction and analysis of complex networks, with applications in diverse domains including climate, ecology, social networks, time series analysis, and computer vision. He is actively involved in shaping an emerging research area called climate informatics, which lies at the intersection of computer science and climate sciences, and his interests are more generally in interdisciplinary research and scientific problems relating to climate and sustainability.
Dr. Steinhaeuser has been awarded one patent and has authored several book chapters as well as numerous peer reviewed articles and papers on these topics. His work has been recognized with multiple awards including two Oak Ridge National Laboratory Significant Event Awards for "Novel Analyses of the Simulation Results from the CCSM 3.0 Climate Model" and "Science Support for a Climate Change War Game and Follow-Up Support to the US Department of Defense."
Table of Contents
Network science perspectives on engineering adaptation to climate change and weather extremes
Udit Bhatia, Auroop R. Ganguly
Structured Estimation in High Dimensions: Applications in Climate
Andre R Goncalves, Arindam Banerjee
Spatiotemporal Global Climate Model Tracking
Scott McQuade, Claire Monteleoni
Statistical Downscaling in Climate with State of the Art Scalable Machine Learning
Thomas Vandal, Udit Bhatia, Auroop R. Ganguly
Large-Scale Machine Learning for Species Distributions
Reid Johnson, Nitesh Chawla
Using Large-scale Machine Learning to Improve our Understanding of the Formation of Tornadoes
Amy McGovern, Corey Potvin, Rodger Brown
Deep Learning for Very High Resolution Imagery Classification
Sangram Ganguly, Saikat Basu, Ramakrishna Nemani, Supratik Mukhopadhyay, Andrew Michaelis, Petr Votava, Cristina Milesi, Uttam Kumar
Unmixing Algorithms: A Review of Techniques for Spectral Detection and Classification of Land Cover from Mixed Pixels on NASA Earth Exchange
Uttam Kumar, Cristina Milesi, S. Kumar Raja, Ramakrishna Nemani, Sangram Ganguly, Weile Wang, Petr Votava, Andrew Michaelis, and Saikat Basu
Semantic Interoperability of Long-Tail Geoscience Resources over the Web
Mostafa M.Elag, Praveen Kumar, Luigi Marini, Scott D. Peckham, Rui Liu