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• W2169921071 abstract "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." @default.
• W2169921071 created "2016-06-24" @default.
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• W2169921071 date "2012-01-01" @default.
• W2169921071 modified "2023-10-16" @default.
• W2169921071 title "Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast" @default.
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• W2169921071 doi "https://doi.org/10.1155/2012/915053" @default.
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