Discharge modeling in compound channels with non-prismatic floodplains using GMDH and MARS models

被引:13
作者
Yonesi, Hojjat Allah [1 ]
Parsaie, Abbas [2 ]
Arshia, Azadeh [1 ]
Shamsi, Zahra [1 ]
机构
[1] Lorestan Univ, Water Engn Dept, Khorramabad, Lorestan Provin, Iran
[2] Shahid Chamran Univ Ahvaz, Fac Water Sci Engn, Ahvaz, Iran
关键词
discharge estimation; non-prismatic compound open channels; soft computing models; FLOW DISCHARGE; PREDICTION;
D O I
10.2166/ws.2022.058
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this study, modeling of discharge was performed in compound open channels with non-prismatic floodplain (CCNPF) using soft computation models including Multivariate Adaptive Regression Splines (MARS) and Group Method of Data Handling (GMDH) and then their results were compared with the multilayer perceptron neural networks (MLPNN). In addition to the total discharge, the discharge separation between the floodplain and main channel was modeled and predicted. The parameters of relative roughness coefficient, the relative area of flow cross-section, relative hydraulic radius, bed slope, the relative width of water surface, relative depth, convergence or divergence angle, relative longitudinal distance as inputs, and discharge were considered as models output. The results demonstrated that the statistical indices of MARS, GMDH, and MLPNN models in the testing stage are R-2 = 0.962(RMSE = 0.003), 0.930(RMSE = 0.004), and 0.933(RMSE = 0.004) respectively. Examination of statistical error indices o shows that all of the developed models have the appropriate accuracy to estimate the flow discharge in CCNPF. Examination of the structure of developed GMDH and MARS models demonstrated that the parameters of relative: roughness, area, hydraulic radius, flow aspect ratio, depth, and angle of convergence or divergence of floodplain have the greatest impact on modeling and estimation of discharge.
引用
收藏
页码:4400 / 4421
页数:22
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