Abstract
In this paper, some popular approaches to combine neural networks and fuzzy logic systems are briefly surveyed. Then, a novel combination model is presented for synthetic integration of rainfall. The presented model consists of four network layers: input layer, membership function constructing layer, inference layer and defuzzification layer. The combination model is trained using three kinds of forecasted rainfall data, produced by gradual regression method, periodic analysis plus multi-layer method and model output statistics method, as inputs and real rainfall as outputs in Zhejiang province from 1980 to 1997. Then the trained model is employed to integrate/forecast the rainfall of Zhejiang province from 1998 to 2000. Integration results show that the presented model can achieve satisfactory forecast performance.
