Modeling of Discharge in Compound open channels with Convergent and Divergent Floodplains Using Soft Computing Methods

In this research, the estimation of discharge in compound open channels with convergent and divergent floodplains using soft computing methods, including the neural fuzzy group method of data handling (NF-GMDH), support vector regression (SVR), and M5 tree algorithm were performed. For this purpose, the geometric and hydraulic characteristics of the flow, including relative roughness (ff), relative area (Ar), relative hydraulic radius (Rr), relative dimension of the flow aspects (δ*), relative width (β), relative flow depth (Dr), relative longitudinal distance (Xr), convergent or divergent angle (θ) of the floodplain and longitudinal slope (So) of the bed were used as input variables and discharge was considered as the target (output) variable. The results showed that the statistical indices of the NF-GMDH in the testing stage are RMSENF-GMDH = 0.004, R2NF-GMDH = 0.923 and in the same stage for SVR are RMSESVR= 0.002 and R2SVR = 0.941 and finally for M5 tree algorithm are RMSEM5 = 0.002, R2M5= 0.931. The evaluation of the structure of the M5 tree algorithm showed that the most effective parameters are ff, Dr, Rr, δ*, and θ which confirm the important parameters specified by MARS, GMDH, and GEP algorithms used by previous researchers.