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Mathematical Problems in Engineering
Volume 2012, Article ID 915053, 12 pages
Research Article

Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2Business School, Hohai University, Nanjing 210098, China
3CSIRO Mathematics, Informatics and Statistics, Private Bag No. 5, Wembley, WA 6913, Australia

Received 10 February 2012; Revised 9 August 2012; Accepted 30 August 2012

Academic Editor: Joao B. R. Do Val

Copyright © 2012 Junfei Chen 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.


Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI). We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF-) based forecast model has consistently shown better predictive skills than the ARIMA model for both long and short drought forecasting. The confidence intervals derived from the proposed model generally have good coverage, but still tend to be conservative to predict some extreme drought events.