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Indexed by:期刊论文

Date of Publication:2012-07-01

Journal:WATER SCIENCE AND TECHNOLOGY

Included Journals:SCIE、EI、PubMed、Scopus

Volume:66

Issue:10

Page Number:2090-2098

ISSN No.:0273-1223

Key Words:association rule analysis; flood forecasting; fuzzy reasoning

Abstract:In this paper, a computationally efficient version of the widely used Takagi-Sugeno (T-S) fuzzy reasoning method is proposed, and applied to river flood forecasting. It is well known that the number of fuzzy rules of traditional fuzzy reasoning methods exponentially increases as the number of input parameters increases, often causing prohibitive computational burden. The proposed method greatly reduces the number of fuzzy rules by making use of the association rule analysis on historical data, and therefore achieves computational efficiency for the cases of a large number of input parameters. In the end, we apply this new method to a case study of river flood forecasting, which demonstrates that the proposed fuzzy reasoning engine can achieve better prediction accuracy than the widely used Muskingum-Cunge scheme.