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

Junfei Chen, Qiongji Jin and Jing Chao

Mathematical Problems in Engineering, 2012, vol. 2012, 1-16

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.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:235929

DOI: 10.1155/2012/235929

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