Application of ANFIS to stream-way transition

Authors:
Shih-Wei Ma;Chang-Huan Kou;Li Chen;An-Pei Wang;Cheng-Yuan Sung
Affiliations:
Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsinchu, Taiwan, R.O.C.;Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsinchu, Taiwan, R.O.C.;Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsinchu, Taiwan, R.O.C.;Department of Civil Engineering, Chung Yuan Christian University, Chungli, Taiwan, R.O.C.;Department of Civil Engineering, Chung Yuan Christian University, Chungli, Taiwan, R.O.C.
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Year:
2009

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Abstract

The main purpose of this paper is to predict streamway transition with Adaptive-Network-Based Fuzzy Inference System (ANFIS). Therefore, the downstream stream-way transition according to the upstream conditions is forecasted by ANFIS. Five main factors may affect the stream-way transition include inflow position, inflow angle, slope, flow discharge, and sand content of suspended sediment. We selected some cross sections of Ta-Chia River in Taiwan as a case study. The results show that ANFIS has better performance than the traditional linear regression method (LR).