Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques

被引：4

作者：

Hussain, Athar ^{[1
]}

Singh, Jatin Kumar ^{[2
]}

Kumar, A. R. Senthil ^{[3
]}

Harne, K. R. ^{[1
]}

机构：

[1] CBP Govt Engn Coll, Delhi, India

[2] Gautam Buddha Univ, Sch Engn, Environm Engn Sect, Civil Engn Dept, Greater Noida, India

[3] Natl Inst Hydrol, Roorkee, Uttar Pradesh, India

来源：

INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS | 2019年 / 10卷 / 02期

关键词：

Artificial Neural Networks; Fuzzy Logic; Multilayer Perception; Radial Basis Function; Rainfall-Runoff Modelling; ARTIFICIAL NEURAL-NETWORKS; FUZZY-LOGIC; HYDROGRAPH;

D O I：

10.4018/IJAEIS.2019040101

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The prediction of the runoff generated within a watershed is an important input in the design and management of water resources projects. Due to the tremendous spatial and temporal variability in precipitation, rainfall-runoff relationship becomes one of the most complex hydrologic phenomena. Under such circumstances, using soft computing approaches have proven to be an efficient tool in modeling of runoff. These models are capable of predicting river runoff values that can be used for hydrologic and hydraulic engineering design and water management purposes. It has been observed that the artificial neural networks (ANN) model performed well compared to other soft computing techniques such as fuzzy logic and radial basis function investigated in this study. In addition, comparison of scatter plots indicates that the values of runoff predicted by the ANN model are more precise than those found by RBF or Fuzzy Logic model.

引用

页码：1 / 20

页数：20

共 36 条

[1] An artificial neural network model for generating hydrograph from hydro-meteorological parameters [J].

Ahmad, S ;

Simonovic, SP .

JOURNAL OF HYDROLOGY, 2005, 315 (1-4) :236-251

[2] Water level forecasting through fuzzy logic and artificial neural network approaches [J].

Alvisi, S ;

Mascellani, G ;

Franchini, M ;

Bárdossy, A .

HYDROLOGY AND EARTH SYSTEM SCIENCES, 2006, 10 (01) :1-17

[3]

[Anonymous], 1980, APPL MODELING HYDROL, DOI DOI 10.1002/9781118445112.STAT07809

[4] Fuzzy unit hydrograph -: art. no. W02401 [J].

Bárdossy, A ;

Mascellani, G ;

Franchini, M .

WATER RESOURCES RESEARCH, 2006, 42 (02)

[5] Artificial neural network approach to flood forecasting in the River Arno [J].

Campolo, M ;

Soldati, A ;

Andreussi, P .

HYDROLOGICAL SCIENCES JOURNAL, 2003, 48 (03) :381-398

[6]

Chiu S., 1994, J INTELL FUZZY SYST, V2, P267, DOI [10.3233/IFS-1994-2306, DOI 10.3233/IFS-1994-2306]

[7] Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data [J].

Cobaner, M. ;

Unal, B. ;

Kisi, O. .

JOURNAL OF HYDROLOGY, 2009, 367 (1-2) :52-61

[8]

Dawson CW, 2001, PROG PHYS GEOG, V25, P80, DOI 10.1191/030913301674775671

[9] Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation [J].

de Vos, NJ ;

Rientjes, THM .

HYDROLOGY AND EARTH SYSTEM SCIENCES, 2005, 9 (1-2) :111-126

[10] A fuzzy neural network model for deriving the river stage-discharge relationship [J].

Deka, P ;

Chandramouli, V .

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2003, 48 (02) :197-209

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