Prediction of hydrological phenomena using self-organizing mathematical models

Prediction of hydrological phenomena using self-organizing mathematical models The paper considers the application of self-organizing models, specifically, the method of grouped arguments consideration (MGAC), to forecast short and non-stationary time series of observations in the ocean. A sequence of operations for the treatment of observational series is suggested. To assess its efficiency, we have used mean monthly oxygen concentration data collected in the surface and near-bottom layers of the Taganrog Bay. It is shown that the application of the MGAC model allows one to reduce by two times the root-mean-square error of that of the series prediction by five points, in comparison with the Jenkins-Box regressional model. It has been concluded that the predictors' non-linear functions may be effectively used in the treatment of short samplings. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Physical Oceanography Springer Journals

Prediction of hydrological phenomena using self-organizing mathematical models

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 1997 by VSP
Subject
Earth Sciences; Oceanography; Remote Sensing/Photogrammetry; Atmospheric Sciences; Climate Change; Environmental Physics
ISSN
0928-5105
eISSN
0928-5105
D.O.I.
10.1007/BF02523070
Publisher site
See Article on Publisher Site

Abstract

The paper considers the application of self-organizing models, specifically, the method of grouped arguments consideration (MGAC), to forecast short and non-stationary time series of observations in the ocean. A sequence of operations for the treatment of observational series is suggested. To assess its efficiency, we have used mean monthly oxygen concentration data collected in the surface and near-bottom layers of the Taganrog Bay. It is shown that the application of the MGAC model allows one to reduce by two times the root-mean-square error of that of the series prediction by five points, in comparison with the Jenkins-Box regressional model. It has been concluded that the predictors' non-linear functions may be effectively used in the treatment of short samplings.

Journal

Physical OceanographySpringer Journals

Published: Oct 21, 2006

References

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