DocumentCode :
3591300
Title :
Special session panel discussion: methodological issues in the application of learning methods to climate modeling and Earth sciences
Author :
Cherkassky, Vladimir ; Krasnopolsky, Vladimir M. ; Solomatine, D.P. ; Vald?©s, Julio J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Minnesota Univ., USA
Volume :
3
fYear :
2005
Abstract :
Summary form only given. 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 includes informal presentations by the session co-chairs followed by questions and answers from the audience. Topics of discussion include the following: 1) to identify major types of problems encountered in this field; 2) how to estimate the quality of data-driven models; 3) what are specific characteristics of data sets in climate modeling/Earth sciences that make them different from other applications; and 4) try to come to an agreement on possible benchmark data sets in this field.
Keywords :
climatology; data analysis; geophysics computing; hydrology; meteorology; oceanography; Earth science; climate modeling; data-driven modeling; geophysical data processing; hydrology; learning method; meteorological application; oceanographic application;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
Type :
conf
DOI :
10.1109/IJCNN.2005.1556140
Filename :
1556140
Link To Document :
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