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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Large-Scale Machine Learning in the Earth Sciences (Paperback)
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Large-Scale Machine Learning in the Earth Sciences (Paperback)
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Expected to ship within 9 - 15 working days
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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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