Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

• Published in: Hydrology and earth system sciences Vol. 21; no. 8; pp. 4021 - 4036
• Main Authors: Wani, Omar, Beckers, Joost V. L, Weerts, Albrecht H, Solomatine, Dimitri P
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 10.08.2017; Copernicus Publications
• Subjects: Case studies; Clustering; Decision making; Errors; Forecasting; Hydrologic forecasting; Hydrologie en Kwantitatief Waterbeheer; Hydrology; Hydrology and Quantitative Water Management; Hydrometeorology; Intervals; Learning; Leerstoelgroep Hydrologie en kwantitatief waterbeheer; Machine learning; Methods; Microprocessors; Parametric methods; Precipitation; Probability distribution; Probability theory; Processors; Random variables; Regression analysis; Reliability; Resampling; Rivers; Statistical analysis; Stream discharge; Stream flow; Streamflow forecasting; Test procedures; Time series; Uncertainty; Weather forecasting; WIMEK; United Kingdom > UK; Severn River
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-21-4021-2017

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.