Quantifying Uncertainty of Flood Forecasting Using Data Driven Models

Flooding is a complex and inherently uncertain phenomenon. Consequently forecasts of it are inherently uncertain in nature due to various sources of uncertainty including model uncertainty, input uncertainty and parameter uncertainty. Several approaches have been reported to quantify and propagate uncertainty through flood forecasting models using probabilistic and fuzzy set theory based methods. In this paper, a method for quantifying uncertainty in flood forecasting using data driven modeling techniques is presented. Uncertainty of the model outputs is estimated in terms of prediction intervals. First, prediction intervals for training (calibration) data using clustering techniques to identify the distinguishable regions in input space with similar distributions of model errors were constructed. Secondly, a data driven model was built using the computed prediction intervals as targets. Thirdly, the constructed model was validated by estimating the prediction intervals for unseen (test) data. The method was tested on Sieve and Bagmati river basins using various data driven models. Preliminary results show that the method is superior to another method (uniform interval method) estimating prediction intervals.