Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections

摘要

Flow measurement in irrigation and drainage networks and water conveyance channels have particular importance. Direct methods of flow measurement are costly, time-consuming and are generally associated with losses of energy in flow. In this study, estimation of discharge and end depth of free overfall flows in trapezoidal channels section were investigated. For this purpose, data-driven techniques including dynamic evolving neural-fuzzy inference system (DENFIS), multivariate adaptive regression spline (MARS) and M5 model tree (M5Tree) were developed. 189 laboratory data experiments, six different scenarios based on geometric variables including side slope (m), bed width (B), bed slope (S-0), and hydraulic variables including critical depth (Y-c), critical slope (S-c) and end depth (Y-E) or discharge (Q) were applied. The model's performance was evaluated thorough several statistical indicators and graphical presentations. The accuracy of all three models were apparent in estimation of the discharge and the end depth for most of the scenarios. The results showed that the DENFIS model for the Input combination of all variables (Y-c, Y-E, B, S-0, m, S-c) with the maximum values of R-2 and Nash-Sutcliffe efficiency coefficient (NSE) that were equal to 0.976 and 0.975, respectively, and the minimum values of RMSE, MAE, PBIAS and RSR, that were equal to 0.0015, 0.0989, -1.5906, and 0.1574, respectively, showed the highest estimation accuracy. Regarding the end depth estimation, DENFIS model for the input combination including the variables Y-c, Q, S-c, m, B with the highest values of R-2 and NSE equal to 0.993 and 0.992 respectively, and the lowest values of RMSE, MAE, PBIAS and RSR equal to 0.0028, 0.1628, 0.7383 and 0.0883, respectively, had a better performance compared to other MARS and M5Tree. The results of this study suggest DENFIS as a suitable and powerful model for estimation of discharge in irrigation and drainage networks.