DOI: 10.5194/hess-21-4021-2017
Scopus记录号: 2-s2.0-85027305702
论文题名: Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting
作者: Wani O ; , Beckers J ; V ; L ; , Weerts A ; H ; , Solomatine D ; P
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 8 起始页码: 4021
结束页码: 4036
语种: 英语
Scopus关键词: Forecasting
; Nearest neighbor search
; Deterministic modeling
; Hydrologic forecasting
; Hydrometeorological conditions
; Instance based learning
; Streamflow forecasting
; Uncertainty distributions
; Uncertainty estimation
; Uncertainty estimators
; Uncertainty analysis
; estimation method
; forecasting method
; hydrological modeling
; hydrological response
; hydrometeorology
; learning
; probability
; streamflow
; uncertainty analysis
; Brue Basin
; England
; Severn River [United Kingdom]
; Somerset
; United Kingdom
英文摘要: A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79089
Appears in Collections: 气候变化事实与影响
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作者单位: IHE Delft Institute for Water Education, Delft, Netherlands; Deltares, Delft, Netherlands; Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, Netherlands; Water Resources Section, Delft University of Technology, Delft, Netherlands; Water Problems Institute of RAS, Moscow, Russian Federation; Institute of Environmental Engineering, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland; Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland
Recommended Citation:
Wani O,, Beckers J,V,et al. Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting[J]. Hydrology and Earth System Sciences,2017-01-01,21(8)