Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention

The Application of Gray Model and BP Artificial Neural Network in Predicting Drought in the Liaoning Province

Authors
Xiaojing Liu, Yongpeng Song, Donglai Ma, Peng Chen
Corresponding Author
Xiaojing Liu
Available Online November 2016.
DOI
https://doi.org/10.2991/rac-16.2016.44How to use a DOI?
Keywords
Gray Model; BP artificial neural network; precipitation prediction
Abstract
Precipitation prediction is the core of a regional drought prediction. Due to the great randomness and uncertainty in the precipitation process, this study combined the grey model and BP artificial neural network. The residual errors of precipitation were modified by the BP artificial neural network after the precipitation were modeled and predicted by the grey model, then the grey-BP neural network combination model was established for predicting the precipitation in the studied area. The results showed that the prediction accuracy of the combination model was the highest by integrating the advantages of the grey model and BP artificial neural network. The prediction error of the combination model was much lower than the grey model's and only slightly lower than the BP neural network model's.
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Proceedings
7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC-2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-242-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/rac-16.2016.44How 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  - Xiaojing Liu
AU  - Yongpeng Song
AU  - Donglai Ma
AU  - Peng Chen
PY  - 2016/11
DA  - 2016/11
TI  - The Application of Gray Model and BP Artificial Neural Network in Predicting Drought in the Liaoning Province
BT  - 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC-2016)
PB  - Atlantis Press
SP  - 276
EP  - 279
SN  - 1951-6851
UR  - https://doi.org/10.2991/rac-16.2016.44
DO  - https://doi.org/10.2991/rac-16.2016.44
ID  - Liu2016/11
ER  -