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

DOI

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM 2013)
PB  - Atlantis Press
SP  - 51
EP  - 55
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsem.2013.11
DO  - 10.2991/icsem.2013.11
ID  - Liu2013/04
ER  -