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Machine Learning Methods in the Environmental Sciences - Neural Networks and Kernels (Hardcover)
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Machine Learning Methods in the Environmental Sciences - Neural Networks and Kernels (Hardcover)
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Machine learning methods originated from artificial intelligence
and are now used in various fields in environmental sciences today.
This is the first single-authored textbook providing a unified
treatment of machine learning methods and their applications in the
environmental sciences. Due to their powerful nonlinear modeling
capability, machine learning methods today are used in satellite
data processing, general circulation models(GCM), weather and
climate prediction, air quality forecasting, analysis and modeling
of environmental data, oceanographic and hydrological forecasting,
ecological modeling, and monitoring of snow, ice and forests. The
book includes end-of-chapter review questions and an appendix
listing web sites for downloading computer code and data sources. A
resources website containing datasets for exercises, and
password-protected solutions are available. The book is suitable
for first-year graduate students and advanced undergraduates. It is
also valuable for researchers and practitioners in environmental
sciences interested in applying these new methods to their own
work. Preface Excerpt Machine learning is a major subfield in
computational intelligence (also called artificial intelligence).
Its main objective is to use computational methods to extract
information from data. Neural network methods, generally regarded
as forming the first wave of breakthrough in machine learning,
became popular in the late 1980s, while kernel methods arrived in a
second wave in the second half of the 1990s. This is the first
single-authored textbook to give a unified treatment of machine
learning methods and their applications in the environmental
sciences. Machine learning methods began to infiltrate the
environmental sciences in the 1990s. Today, thanks to their
powerful nonlinear modeling capability, they are no longer an
exotic fringe species, as they are heavily used in satellite data
processing, in general circulation models (GCM), in weather and
climate prediction, air quality forecasting, analysis and modeling
of environmental data, oceanographic and hydrological forecasting,
ecological modeling, and in the monitoring of snow, ice and
forests, etc. This book presents machine learning methods and their
applications in the environmental sciences (including satellite
remote sensing, atmospheric science, climate science, oceanography,
hydrology and ecology), written at a level suitable for beginning
graduate students and advanced undergraduates. It is also valuable
for researchers and practitioners in environmental sciences
interested in applying these new methods to their own work.
Chapters 1-3, intended mainly as background material for students,
cover the standard statistical methods used in environmental
sciences. The machine learning methods of chapters 4-12 provide
powerful nonlinear generalizations for many of these standard
linear statistical methods. End-of-chapter review questions are
included, allowing readers to develop their problem-solving skills
and monitor their understanding of the material presented. An
appendix lists websites available for downloading computer code and
data sources. A resources website is available containing datasets
for exercises, and additional material to keep the book completely
up-to-date. About the Author WILLIAM W. HSIEH is a Professor in the
Department of Earth and Ocean Sciences and in the Department of
Physics and Astronomy, as well as Chair of the Atmospheric Science
Programme, at the University of British Columbia. He is
internationally known for his pioneering work in developing and
applying machine learning methods in environmental sciences. He has
published over 80 peer-reviewed journal publications covering areas
of climate variability, machine learning, oceanography, atmospheric
science and hydrology.
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