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Advances in Meteorology
Volume 2016, Article ID 9472605, 10 pages
Research Article

A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook

1Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
2Department of Agricultural Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan
3Department of Civil Engineering, Chonbuk National University, Jeonju 54896, Republic of Korea
4Department of Civil and Environmental Engineering, Hanyang University, Ansan 15588, Republic of Korea

Received 25 September 2015; Revised 17 January 2016; Accepted 21 February 2016

Academic Editor: Ji Chen

Copyright © 2016 Ji Yae Shin 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.


Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF) model was designed using the past, current, and forecasted drought condition. In this study, the drought conditions were represented by the standardized precipitation index (SPI). The accuracy of forecasted SPIs was assessed by comparing the observed SPIs and confidence intervals (CIs), exhibiting the associated uncertainty. Then, this study suggested the drought outlook framework based on probabilistic drought forecasting results. The overall results provided sufficient agreement between the observed and forecasted drought conditions in the outlook framework.