ANFIS-based approach for predicting sediment transport in clean sewer

被引：125

作者：

Azamathulla, H. Md ^{[1
]}

Ghani, Aminuddin Ab. ^{[1
]}

Fei, Seow Yen ^{[2
]}

机构：

[1] Univ Sains Malaysia, River Engn & Urban Drainage Res Ctr REDAC, Nibong Tebal 14300, Penang, Malaysia

[2] Univ Sains Malaysia, Sch Civil Engn, Nibong Tebal 14300, Penang, Malaysia

来源：

APPLIED SOFT COMPUTING | 2012年 / 12卷 / 03期

关键词：

Sewer pipes; Sewer sediments; Sediment transport; Regression analysis; ANFIS; FUZZY;

D O I：

10.1016/j.asoc.2011.12.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r(2) = 0.98 and RMSE = 0.002431) compared to the existing predictor. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：1227 / 1230

页数：4

共 15 条

[1]

Ab Ghani A., 2011, ASCE J PIPELINE SYST, V2, P102

[2]

Ab Ghani A., 1993, THESIS U NEWCASTLE U

[3]

American Society of Civil Engineers (ASCE), 1970, AM SOC CIV ENG MAN R, V37

[4]

[Anonymous], 1995, Fuzzy Logic Toolbox User's Guide R2012a

[5]

Cigizoglu Hikmet Kerem, 2002, Turkish Journal of Engineering and Environmental Sciences, V26, P27

[6] From flumes to rivers: Can sediment transport in natural alluvial channels be predicted from observations at the laboratory scale? [J].

Dogan, Emrah ;

Tripathi, Shivam ;

Lyn, Dennis A. ;

Govindaraju, Rao S. .

WATER RESOURCES RESEARCH, 2009, 45

[7] ANFIS - ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM [J].

JANG, JSR .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1993, 23 (03) :665-685

[8] Suspended sediment estimation using neuro-fuzzy and neural network approaches [J].

Kisi, O .

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2005, 50 (04) :683-696

[9] Development of design methodology for self-cleansing sewers [J].

May, RWP ;

Ackers, JC ;

Butler, D ;

John, S .

WATER SCIENCE AND TECHNOLOGY, 1996, 33 (09) :195-205

[10] SEDIMENT TRANSPORT IN RIGID BED CONVEYANCES [J].

MAYERLE, R ;

NALLURI, C ;

NOVAK, P .

JOURNAL OF HYDRAULIC RESEARCH, 1991, 29 (04) :475-495

← 1 2 →