Method for estimating parameter γ in a two-dimensional approximate furrow infiltration model

This study expands the analysis of the parameter γ in the approximate furrow infiltration model (FIM) proposed by Bautista et al. On the basis of the obtained results, a gray wolf optimization (GWO)–backpropagation neural network (BPNN)–adaptive boosting (AdaBoost) regression model was developed, and its γ prediction performance was compared with that of a BPNN model and BPNN–AdaBoost model. The results indicated that furrow cross section, soil texture, and water depth (h₀) considerably influence the edge effect (ΔI) and γ, with γ ranging from 0.60 to 0.94. Moreover, the edge effect’s relative contribution to the total furrow infiltration is 21.0 %–41.7 %. Three performance measures, namely Bias, root mean square error (RMSE), and coefficient of determination (R²), were employed to evaluate the performance of the proposed models. The results revealed that γ values of Bias, RMSE, and R² were –0.0009, 0.062, and 0.851 for the GWO–BPNN–AdaBoost model has the high accuracy, respectively, with furrow depth (D), bottom width (B), top width (T), n, α, saturated hydraulic conductivity (K_s), h₀, effective saturation (S_e) as input factors. The two-dimensional cumulative infiltration was calculated using the γ values predicted by the models. Among these predictions, those produced by the GWO–BPNN–AdaBoost model most closely aligned with the simulated values acquired using Hydrus-2D, with the Bias, RMSE, and R² values being –0.53, 1.41 cm, and 0.993, respectively. Based on analysis of the obtained results, it is evident that GWO–BPNN–AdaBoost can estimate γ more accurately.