Fuzzy neural networks for water level and discharge forecasting with uncertainty

The application of the model to two specific cases and a comparison of the results with those provided by other data-driven models - Bayesian neural networks (Neal, 1992) and the Local Uncertainty Estimation Model (Shrestha and Solomatine, 2006) - show its effectiveness in estimating water levels or discharges under uncertainty. The fuzzy neural network enables us to define bands that are expected to contain given percentages of forecasted level/discharge values for each lead time selected; an analysis of the results obtained reveals that these bands (e.g., 99%, 95% and 90% uncertainty bands) generally have a slightly smaller width compared to the bands obtained using other data-driven models.