Development of stage-discharge rating curve using ANN

被引:9
作者
Chaplot, Barkha [1 ]
Birbal, Prashant [2 ]
机构
[1] Babasaheb Bhimrao Ambedkar Bihar Univ, MJK Coll, Dept Geog, Bettiah, Muzaffarpur, India
[2] Univ West Indies, Dept Civil & Environm Engn, St Augustine, Trinidad Tobago
关键词
stage-discharge; neural networks; rating curve; regression; modelling; NEURAL-NETWORKS; RIVER; TREES; FUZZY; MODEL;
D O I
10.1504/IJHST.2022.123643
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Accurate forecasting of river discharge is essential for the efficient operation of water resources systems. Therefore, researchers are consistently developing and improving various techniques to predict river discharge with relative ease and high accuracy, although traditional methods are available. This paper presents mainly three data-driven modelling techniques, namely the stage rating curve (SRC), generalised reduced gradient (GRG) solver, and an artificial neural network (ANN) to accurately model the stage-discharge relationship for local rivers in Trinidad and Tobago using only low flow data. The model that produced the overall superior results was the ANN.
引用
收藏
页码:75 / 95
页数:21
相关论文
共 27 条
[1]   Modeling of stage–discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi–Sugeno inference system technique: a comparative study [J].
Al-Abadi A.M. .
Applied Water Science, 2016, 6 (04) :407-420
[2]  
[Anonymous], 1999, Neural networks: a comprehensive foundation
[3]   Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River [J].
Azamathulla, Hazi Mohammad ;
Ghani, Aminuddin Ab. ;
Leow, Cheng Siang ;
Chang, Chun Kiat ;
Zakaria, Nor Azazi .
WATER RESOURCES MANAGEMENT, 2011, 25 (11) :2901-2916
[4]   Application of excel solver for parameter estimation of the nonlinear Muskingum models [J].
Barati, Reza .
KSCE JOURNAL OF CIVIL ENGINEERING, 2013, 17 (05) :1139-1148
[5]   Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran [J].
Barzegar, Rahim ;
Adamowski, Jan ;
Moghaddam, Asghar Asghari .
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2016, 30 (07) :1797-1819
[6]  
Braca G., 2008, 11 FORALPS U STUD TR
[7]   Accuracy of annual volume from current-meter-based stage discharges [J].
Clemmens, Albert J. ;
Wahlin, Brian T. .
JOURNAL OF HYDROLOGIC ENGINEERING, 2006, 11 (05) :489-501
[8]   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
[9]   New Approach for Stage-Discharge Relationship: Gene-Expression Programming [J].
Guven, Aytac ;
Aytek, Ali .
JOURNAL OF HYDROLOGIC ENGINEERING, 2009, 14 (08) :812-820
[10]   Stage-discharge relations for low-gradient tidal streams using data-driven models [J].
Habib, EH ;
Meselhe, EA .
JOURNAL OF HYDRAULIC ENGINEERING, 2006, 132 (05) :482-492