An Integrated Neuro-Fuzzy-Statistical Approach to Hydrological Modelling

This paper presents four different methodologies for integrating conventional and AI-based methods to provide a hybridised solution to the problem of generating continuous river flow and flood forecasting estimates. Individual models ranging from neural networks to statistical predictors were developed on a standalone basis using historical time series data for gauging stations on the River Ouse in North Yorkshire and the Upper River Wye in Central Wales. A simple linguistic fuzzy logic model for Skelton, and TOPMODEL predictions for the Upper Wye, were also incorporated as additional model inputs. Each of these individual models were then integrated using four different approaches: an averaging procedure, a Bayesian approach, and two fuzzy logic models, the first based on just current and past river flow conditions, and the second on a fuzzification of the crisp Bayesian approach. Model performance was assessed via global statistics and more specific flood related evaluation measures. The addition of fuzzy logic to the crisp Bayesian model yielded overall results that were superior to both the individual model estimates and the other integrated approaches.