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

Title
A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook
Author
김태웅
Keywords
METEOROLOGICAL DROUGHT; SEASONAL PREDICTION; UNITED-STATES; SOUTH-KOREA; MULTIMODEL ENSEMBLE; NEURAL-NETWORKS; MODEL; RISK
Issue Date
2016-06
Publisher
HINDAWI PUBLISHING CORP
Citation
ADVANCES IN METEOROLOGY, v. 2016, Article no. 9472605
Abstract
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.
URI
https://www.hindawi.com/journals/amete/2016/9472605/abs/http://hdl.handle.net/20.500.11754/65491
ISSN
1687-9309; 1687-9317
DOI
10.1155/2016/9472605
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > CIVIL AND ENVIRONMENTAL ENGINEERING(건설환경공학과) > Articles
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