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 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 R2 = 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.

Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models

“…The GMDH structure is determined systematically, unlike the perceptron multilayer neural network model whose structure is mainly developed based on the designer's experience (Saberi-Movahed et al 2020). Currently, the GMDH model is successfully used in most hydraulic engineering problems (Yonesi et al 2022), such as predicting the scour around hydraulic structures (Nou et al 2022) and the discharge coefficient of weirs (Parsaie and Haghiabi 2020). The GMDH model was developed based on the extension of partial descriptions.…”

“…When the flood passes through an alluvial river, some changes occur in the bed forms due to flow conditions. Thus, the friction factor can change even more than ten times during a flood (Vanoni 2006).…”

Prediction of shear stress distribution in compound channel with smooth converging floodplains

“…In their recent study, Naik et al (2022) introduced a unique equation that utilizes GEP to forecast the water surface profile in converging compound channels by including geometric factors. In their work, Yonesi et al (2022) investigated the modelling of discharge in compound open channels with non-prismatic floodplains (CCNPF) using soft computation models, namely MARS and GMDH. The results were afterward compared with those produced from MLPNN.…”

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

“…Today, using numerical models as an effcient tool for simulation and then studying complex natural processes open the way for many technical and engineering issues, including the state of the sea. Soft computing methods such as model tree (MT), gene expression programming (GEP), multivariate adaptive regression spline (MARS), adaptive-neuro fuzzy inference system (ANFIS), and Bayesian Network (BN) have proven successful applications for modeling various ocean engineering problems [11][12][13][14][15][16]. In addition, many studies demonstrated the combination of properties of diferent soft computing methods with evolutionary algorithms causing an improvement in the power prediction of phenomena in solving ocean environment problems [17][18][19][20][21].…”