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