RBF Prediction Model Based on EMD for Forecasting GPS Precipitable Water Vapor and Annual Precipitation

Authors
Yanping Liu, Yong Wang, Zhen Wang
Corresponding Author
Yanping Liu
Available Online April 2013.
DOI
https://doi.org/10.2991/icsem.2013.11How to use a DOI?
Keywords
Empirical Mode Decomposition, RBF Neural Network, GPS Precipitable Water Vapor, Precipitation
Abstract
The forecast of precipitations is important in meteorology and atmospheric sciences. A new model is proposed based on empirical mode decomposition and the RBF neural network. Firstly, GPS PWV time series is broken down into series of different scales intrinsic mode function. Secondly, the phase-space reconstruction is done. Thirdly, each component is predicted by RBF. Finally, the final prediction value is reconstructed. Next, the model is tested on annual precipitation sequence from 2001 to 2010 in northeast China. The result shows that predictive value is close to the actual precipitation, which can better reflect the actual precipitation change. From 2001 to 2010, the maximum deviation of the predicted values never exceeds 4%. The testing results show that the proposed model can increase precipitation forecasting accuracies not only in GPS PWV but also in annual precipitation.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2nd International Conference On Systems Engineering and Modeling (ICSEM-13)
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2013
ISBN
978-94-91216-42-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/icsem.2013.11How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yanping Liu
AU  - Yong Wang
AU  - Zhen Wang
PY  - 2013/04
DA  - 2013/04
TI  - RBF Prediction Model Based on EMD for Forecasting GPS Precipitable Water Vapor and Annual Precipitation
BT  - 2nd International Conference On Systems Engineering and Modeling (ICSEM-13)
PB  - Atlantis Press
SP  - 51
EP  - 55
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsem.2013.11
DO  - https://doi.org/10.2991/icsem.2013.11
ID  - Liu2013/04
ER  -