A NON-LINEAR COMBINATION OF THE FORECASTS OF RAINFALL-RUNOFF MODELS BY THE FIRST-ORDER TAKAGI-SUGENO FUZZY SYSTEM

Abstract

With a plethora of watershed rainfall-runoff models available for flood forecasting and more than adequate computing power to operate a number of such models simultaneously, we can now combine the simulation results from the different models to produce the combination forecasts. In this paper, the first-order Takagi-Sugeno fuzzy system is introduced and explained as the fourth combination method (besides other three combination methods tested earlier, i.e. the simple average method (SAM), the weighted average method (WAM), and the neural network method (NNM)) to combine together the simulation results of five different conceptual rainfall-runoff models in a flood forecasting study on eleven catchments. The comparison of the forecast simulation efficiency of the first-order Takagi-Sugeno combination method with the other three combination methods demonstrates that the first-order Takagi-Sugeno method is just as efficient as both the WAM and the NNM in enhancing the flood forecasting accuracy. Considering its simplicity and efficiency, the first-order Takagi-Sugeno method is recommended for use as the combination system for flood forecasting.