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

Abstract

In this study, modeling of discharge was performed in compound open channels with non-prismatic floodplains (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 R2 = 0.962(RMSE = 0.003), 0.930(RMSE = 0.004), and 0.933(RMSE = 0.004) respectively. Examination of statistical error indices shows that all 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 relative parameters: 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. HIGHLIGHTS Estimation of discharge was performed in compound open channels with non-prismatic floodplains using soft computation models.; Discharge separation between the floodplain and main channel was predicted.; Effective parameters for determining the discharge are introduced.; The accuracy of each of the methods is calculated.; The performance of the developed models was compared with the ANFIS model.;

Keywords

• discharge estimation
• non-prismatic compound open channels
• soft computing models