Adaptive neuro-fuzzy computing technique for suspended sediment estimation

被引：89

作者：

Kisi, Ozgur ^{[1
]}

Haktanir, Tefaruk ^{[1
]}

Ardichoglu, Mehmet ^{[1
]}

Ozturk, Ozgur ^{[1
]}

Yalcin, Ekrem ^{[2
]}

Uludag, Salih ^{[2
]}

机构：

[1] Erciyes Univ, Fac Engn, Dept Civil Engn, TR-38039 Kayseri, Turkey

[2] Elect Power Resources Survey & Dev Adm, Ankara, Turkey

来源：

ADVANCES IN ENGINEERING SOFTWARE | 2009年 / 40卷 / 06期

关键词：

Suspended sediment; Neuro-fuzzy; Neural networks; Rating curves; Estimation; NETWORK; PREDICTION; ALGORITHM; TRANSPORT; SYSTEMS;

D O I：

10.1016/j.advengsoft.2008.06.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in suspended sediment estimation. The monthly streamflow and suspended sediment data from two stations, Kuylus and Salur Koprusu, in Kizilirmak Basin in Turkey are used as case studies. The estimation results obtained by using the neuro-fuzzy technique are tested and compared with those of the artificial neural networks and sediment rating curves. Root mean squared errors, mean absolute errors and correlation coefficient statistics are used as comparing criteria for the evaluation of the models' performances. The comparison results reveal that the neuro-fuzzy models can be employed successfully in monthly suspended sediment estimation. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：438 / 444

页数：7

共 36 条

[1] Suspended sediment prediction using two different feed-forward back-propagation algorithms [J].

Ardiclioglu, Mehmet ;

Kisi, Ozgur ;

Haktanir, Tefaruk .

CANADIAN JOURNAL OF CIVIL ENGINEERING, 2007, 34 (01) :120-125

[2] Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique [J].

Bae, Deg-Hyo ;

Jeong, Dae Myung ;

Kim, Gwangseob .

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2007, 52 (01) :99-113

[3] A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction [J].

Chang, FJ ;

Chen, YC .

JOURNAL OF HYDROLOGY, 2001, 245 (1-4) :153-164

[4] Methods to improve the neural network performance in suspended sediment estimation [J].

Cigizoglu, HK ;

Kisi, Ö .

JOURNAL OF HYDROLOGY, 2006, 317 (3-4) :221-238

[5]

Cigizoglu HK, 2005, NORD HYDROL, V36, P49

[6] Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons [J].

Cigizoglu, HK .

ADVANCES IN WATER RESOURCES, 2004, 27 (02) :185-195

[7] RIVER LOADS UNDERESTIMATED BY RATING CURVES [J].

FERGUSON, RI .

WATER RESOURCES RESEARCH, 1986, 22 (01) :74-76

[8] Improving generalization of artificial neural networks in rainfall-runoff modelling [J].

Giustolisi, O ;

Laucelli, D .

HYDROLOGICAL SCIENCES JOURNAL, 2005, 50 (03) :439-457

[9]

Govindaraju RS, 2000, J HYDROL ENG, V5, P124

[10] TRAINING FEEDFORWARD NETWORKS WITH THE MARQUARDT ALGORITHM [J].

HAGAN, MT ;

MENHAJ, MB .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (06) :989-993

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