title: A fuzzy inference method based on association rule analysis with application to river flood forecasting.
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.
authors.affiliation: School of Civil & Hydraulic Engineering, Dalian University of Technology, Dalian, China. czhang@dlut.edu.cn
authors.forename: Chi
authors.initials: C
authors.lastname: Zhang
authors.forename: Yilun
authors.initials: Y
authors.lastname: Wang
authors.forename: Lili
authors.initials: L
authors.lastname: Zhang
authors.forename: Huicheng
authors.initials: H
authors.lastname: Zhou
mesh_terms: D002681:China; D003198:Computer Simulation; D055868:Floods / Q000706:statistics & numerical data*; D005544:Forecasting / Q000379:methods*; D017143:Fuzzy Logic; D008962:Models, Theoretical; D045483:Rivers