The Application of Neural Networks in the Earth System Sciences : Neural Networks Emulations for Complex Multidimensional Mappings - Krasnopolsky, Vladimir M.; SpringerLink (Online service) (Dordrecht : Springer Netherlands : Imprint: Springer, 2013)
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.
Keywords: Atmospheric sciences; Computational intelligence; Neural networks (Computer science) .; Artificial intelligence; Oceanography; Remote sensing; Atmospheric Sciences; Computational Intelligence; Mathematical Models of Cognitive Processes and Neural Networks; Artificial Intelligence; Oceanography; Remote Sensing/Photogrammetry.