The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings /

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 map...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Krasnopolsky, Vladimir M. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Dordrecht : Springer Netherlands : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:Atmospheric and Oceanographic Sciences Library,
Temas:
Atmospheric science.
Computational intelligence.
Neural networks (Computer science) .
Artificial intelligence.
Oceanography.
Geographic information systems.
Atmospheric Science.
Computational Intelligence.
Mathematical Models of Cognitive Processes and Neural Networks.
Artificial Intelligence.
Ocean Sciences.
Geographical Information System.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Introduction.- Introduction to Mapping and Neural Networks
  • Mapping Examples
  • Some Generic Properties of Mappings
  • MLP NN - A Generic Tool for Modeling Nonlinear Mappings
  • Advantages and Limitations of the NN TechniqueNN Emulations
  • Final remarks
  • Atmospheric and Oceanic Remote Sensing Applications
  • Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals
  • NNs for Emulating Forward Models
  • NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms.-Controlling the NN Generalization and Quality Control of Retrievals
  • Neural Network Emulations for SSM/I Data
  • Using NNs to Go Beyond the Standard Retrieval Paradigm
  • Discussion.-Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather
  • Numerical Modeling Background
  • Hybrid Model Component and a Hybrid Model
  • Atmospheric NN Applications
  • An Ocean Application of the Hybrid Model Approach: Neural Network Emulation of Nonlinear Interactions in Wind Wave Models
  • Discussion
  • NN Ensembles and their applications
  • Using NN Emulations of Dependencies between Model Variables in DAS
  • NN nonlinear multi-model ensembles
  • Perturbed physics and ensembles with perturbed physics
  • Conclusions
  • Comments about NN Technique
  • Comments about other Statistical Learning Techniques.

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