| Summary: | 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 92 === Fuzzy inference systems have been successfully applied in numerous fields since they can effectively model human knowledge and adaptively make decision processes. In this paper, we present an innovative fuzzy exemplar-based inference system (FEIS) for flood forecasting. The FEIS is based on fuzzy inference system with its clustering ability enhanced through the EACH (Exemplar-Aided Constructor of Hyper-rectangles) algorithm, which can effectively simulate human intelligence by learning from experience. The FEIS exhibits three important properties: knowledge extraction from numerical data, knowledge (rule) modeling, and fuzzy reasoning processes. To explore its feasibility and predictive accuracy, a mathematical function and a chaotic time series are trained and validated by the model and also compared with the original EACH module. The results demonstrate that the EACH is suitable for categorization but cannot well present the continuous characteristic of the simulated function, while the FEIS can nicely fit the continuous mathematical function and well forecast the chaotic time series. We then apply the proposed model to predict one-hour ahead water level during flood events in the Lan-Yang River, Taiwan. For the purpose of comparison, the back propagation neural network (BPNN) is also performed. The results show that the FEIS model performs better than the original EACH and the BPNN. The FEIS provides a great learning ability and high predictive accuracy for the water level forecasting.
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