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(Article of periodic en Anglais - 2011)

Document title

A hybrid framework for space-time modeling of environmental data

Authors(s) and Affiliation(s)

CHENG T. ; WANG J. ; LI X. (1) ;
(1) School of Geography and Planning, Sun Yat-sen Univ., Guangzhou, CHINE

Abstract

The article presents a hybrid framework combining machine learning and statistical methods to address a modelisation of the nonlinearities and nonstationarities of environmental space-time series. A four-stage procedure is proposed and it is applied to forecast annual average temperature at 137 national meteorological stations in China. The hybrid framework achieves better forecasting accuracy

Source

Article of periodic

published at : Geographical analysis / ISSN 0016-7363

Editor : Ohio State University Press, Columbus, OH - ETATS-UNIS (1969)

Millesime : 2011, vol. 43, no2 [pp. 188-210]

Bibliographic references : 28 ref.

Collation : 10 fig., 7 tabl.

Language

Anglais

INIST-CNRS, Cote INIST : 17078

Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2011
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