Atmospheric and Oceanographic Sciences Library

The Application of Neural Networks in the Earth System Sciences

Neural Networks Emulations for Complex Multidimensional Mappings

Authors: Krasnopolsky, Vladimir M.

  • The book provides a unifying systematic approach to these types of problems, considering them as nonlinear multidimensional mappings that can be emulated by NNs
  • The book introduces a basic mathematical concept of complex nonlinear mapping and offers a generic approach – NN emulation technique -  for modeling such mappings
  • The book presents a detailed discussion of several types of practical, complex, real life applications developed by the author
  • The discussion helps the reader to understand general concepts and provides numerous technical details that are vitally important for practical implementation of presented methodology
see more benefits

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-94-007-6073-8
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-94-007-6072-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • ISBN 978-94-017-8465-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN – the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references.

 “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (...) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (...) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada)

 
“Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (...) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promises to advance a deeper understanding of best modeling practices in environmental science.” (Dr. Sue Ellen Haupt, National Center for Atmospheric Research, Boulder, USA)

 
“Vladimir Krasnopolsky has written an important and wonderful book on applications of neural networks to replace complex and expensive computational algorithms within Earth System Science models. He is uniquely qualified to write this book, since he has been a true pioneer with regard to many of these applications. (...) Many other examples of creative emulations will inspire not just readers interested in the Earth Sciences, but any other modeling practitioner (...) to address both theoretical and practical complex problems that may (or will!) arise in a complex system."  ” (Prof. Eugenia Kalnay, University of Maryland, USA)




 

Reviews

 “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (...) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (...) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada)

 
“Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (...) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promises to advance a deeper understanding of best modeling practices in environmental science.” (Dr. Sue Ellen Haupt, National Center for Atmospheric Research, Boulder, USA)

 
“Vladimir Krasnopolsky has written an important and wonderful book on applications of neural networks to replace complex and expensive computational algorithms within Earth System Science models. He is uniquely qualified to write this book, since he has been a true pioneer with regard to many of these applications. (...) Many other examples of creative emulations will inspire not just readers interested in the Earth Sciences, but any other modeling practitioner (...) to address both theoretical and practical complex problems that may (or will!) arise in a complex system."  ” (Prof. Eugenia Kalnay, University of Maryland, USA)

"The author’s unique perspective that a theoretical physicist and an environmental scientist makes this book a well-balanced combination of a NN theory and practical applications in two main areas, numerical climate/weather models and satellite remote sensing areas, in which the author has made significant contributions. (...) Among many interesting and practical NN applications, several very important oceanic NN applications, in satellite remote sensing (forward and inverse problems), in ocean wind wave models, and in ocean data assimilation systems, are presented in the book.  These and other examples demonstrate the power and flexibility of the NN technique and show how to apply this technique to real life problems. (Prof. Isaac Ginis, University of Rhode Island, USA)


Table of contents (6 chapters)

  • Introduction

    Krasnopolsky, Vladimir M.

    Pages 1-11

  • Introduction to Mapping and Neural Networks

    Krasnopolsky, Vladimir M.

    Pages 13-46

  • Atmospheric and Oceanic Remote Sensing Applications

    Krasnopolsky, Vladimir M.

    Pages 47-79

  • Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather

    Krasnopolsky, Vladimir M.

    Pages 81-143

  • NN Ensembles and Their Applications

    Krasnopolsky, Vladimir M.

    Pages 145-180

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-94-007-6073-8
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-94-007-6072-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • ISBN 978-94-017-8465-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
The Application of Neural Networks in the Earth System Sciences
Book Subtitle
Neural Networks Emulations for Complex Multidimensional Mappings
Authors
Series Title
Atmospheric and Oceanographic Sciences Library
Series Volume
46
Copyright
2013
Publisher
Springer Netherlands
Copyright Holder
Springer Science+Business Media Dordrecht(outside the USA) 2013
eBook ISBN
978-94-007-6073-8
DOI
10.1007/978-94-007-6073-8
Hardcover ISBN
978-94-007-6072-1
Softcover ISBN
978-94-017-8465-8
Series ISSN
1383-8601
Edition Number
1
Number of Pages
XVII, 189
Topics