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

• Main Author: Krasnopolsky, Vladimir M. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: Dordrecht : Springer Netherlands : Imprint: Springer, 2013.
• Series: Atmospheric and Oceanographic Sciences Library, 46
• Subjects: Geography.; Oceanography.; Remote sensing.; Artificial intelligence.; Engineering.; Earth Sciences.; Atmospheric Sciences.; Computational Intelligence.; Mathematical Models of Cognitive Processes and Neural Networks.; Artificial Intelligence (incl. Robotics).; Remote Sensing/Photogrammetry.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-94-007-6073-8

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...

Table of Contents:

• 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.