Statistical uncertainty estimation using random forests and its application to drought forecast
type: Item
instance of: scholarly article
title: Statistical uncertainty estimation using random forests and its application to drought forecast
zbMATH Open document ID: 1264.86013
DOI: 10.1155/2012/915053
author: Junfei Chen
author: Ming Li
author: Weiguang Wang
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 86A32
zbMATH DE Number: 6173687
describes a project that uses: pyuvdata
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/915053
OpenAlex ID: W2169921071
Wikidata QID: Q58912666
cites work: Stochastic renewal model of low-flow streamflow sequences
cites work: Drought forecasting using stochastic models
cites work: Q4137964
cites work: Bagging predictors
cites work: Arcing classifiers. (With discussion)
cites work: Random forests
description: scientific article; zbMATH DE number 6173687