design of deep belief networks for short-term prediction of drought index using data in the huaihe river basin

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2012
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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.
Reference Key
chen2012mathematicaldesign Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Junfei Chen;Qiongji Jin;Jing Chao
Journal journal of power sources
Year 2012
DOI
10.1155/2012/235929
URL
http://dx.doi.org/10.1155/2012/235929
Keywords
civil engineering (general)mathematics engineering (general)

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