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

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  • Junfei Chen
  • Qiongji Jin
  • Jing Chao

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.

Suggested Citation

  • Junfei Chen & Qiongji Jin & Jing Chao, 2012. "Design of Deep Belief Networks for Short-Term Prediction of Drought Index Using Data in the Huaihe River Basin," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-16, May.
    • Handle: RePEc:hin:jnlmpe:235929
    DOI: 10.1155/2012/235929
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    Cited by:

    1. Amogh Gyaneshwar & Anirudh Mishra & Utkarsh Chadha & P. M. Durai Raj Vincent & Venkatesan Rajinikanth & Ganapathy Pattukandan Ganapathy & Kathiravan Srinivasan, 2023. "A Contemporary Review on Deep Learning Models for Drought Prediction," Sustainability, MDPI, vol. 15(7), pages 1-31, April.

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