Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels By William W. Hsieh

Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels

Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels By William W. Hsieh
Publisher: Cambridge University Press 2009-08-31 | 364 Pages | ISBN: 0521791928 | PDF | 4.7 MB

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.

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