Application of ANFIS to Stream-Way Transition

Author

Ma, Shih-Wei ; Kou, Chang-Huan ; Chen, Li ; Wang, An-Pei ; Sung, Cheng-Yuan

Author_Institution

Dept. of Civil Eng. & Eng. Inf., Chung Hua Univ., Hsinchu, Taiwan

Volume

3

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

54

Lastpage

58

Abstract

The main purpose of this paper is to predict stream-way 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).

Keywords

forecasting theory; fuzzy neural nets; inference mechanisms; regression analysis; rivers; ANFIS; Ta-Chia River; adaptive-network-based fuzzy inference system; flow discharge; inflow angle; inflow position; linear regression method; sand content; slope; stream-way transition; suspended sediment; Arteries; Artificial neural networks; Civil engineering; Floods; Fuzzy systems; Informatics; Knowledge engineering; Linear regression; Rivers; Typhoons; adaptive-network-based fuzzy inference system; linear regression method; stream-way transition;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.637

Filename

5358891