Special session panel discussion: Methodological issues in the application of learning methods to climate modeling and earth sciences

Vladimir Cherkassky, Vladimir M. Krasnopolsky, D. P. Solomatine, Julio J. Valdés

Research output: Contribution to conferencePaperpeer-review

Abstract

This special session has been motivated by the growing importance of data-driven modeling in Earth Sciences, Climate Modeling, Meteorological and Oceanographic Applications, Geophysical Data Processing, and Hydrology. Of particular interest are the methodological aspects of learning methods, with the clarification of the advantages and limitations of learning techniques in the context of specific applications. This panel will include informal presentations by the session co-chairs followed by questions and answers from the audience. Topics of discussion include the following: to identify major types of problems encountered in this field; how to estimate the quality of data-driven models; what are specific characteristics of data sets in Climate Modeling/ Earth Sciences that make them different from other applications; try to come to an agreement on possible benchmark data sets in this field.

Original languageEnglish (US)
Pages1728
Number of pages1
DOIs
StatePublished - 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period7/31/058/4/05

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