Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 235929, 16 pages
http://dx.doi.org/10.1155/2012/235929
Research Article

Design of Deep Belief Networks for Short-Term Prediction of Drought Index Using Data in the Huaihe River Basin

Junfei Chen,1,2 Qiongji Jin,1,2 and Jing Chao3

1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2Business School, Hohai University, Nanjing 210098, China
3State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China

Received 18 January 2012; Revised 20 February 2012; Accepted 21 February 2012

Academic Editor: Ming Li

Copyright © 2012 Junfei Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the global climate change, drought disasters occur frequently. Drought prediction is an important content for drought disaster management, planning and management of water resource systems of a river basin. In this study, a short-term drought prediction model based on deep belief networks (DBNs) is proposed to predict the time series of different time-scale standardized precipitation index (SPI). The DBN model is applied to predict the drought time series in the Huaihe River Basin, China. Compared with BP neural network, the DBN-based drought prediction model has shown better predictive skills than the BP neural network for the different time-scale SPI. This research can improve drought prediction technology and be helpful for water resources managers and decision makers in managing drought disasters.