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Fuzzy exemplar‐based inference system for flood forecasting
Author(s) -
Chang LiChiu,
Chang FiJohn,
Tsai YaHsin
Publication year - 2005
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2004wr003037
Subject(s) - computer science , adaptive neuro fuzzy inference system , artificial intelligence , inference , data mining , machine learning , robustness (evolution) , fuzzy logic , flood forecasting , neuro fuzzy , artificial neural network , fuzzy inference system , flood myth , cluster analysis , fuzzy control system , geography , biochemistry , chemistry , archaeology , gene
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 a fuzzy inference system, with its clustering ability enhanced through the 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. The proposed model is employed to predict streamflow 1 hour ahead 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 BPNN. The FEIS provides a great learning ability, robustness, and high predictive accuracy for flood forecasting.