Pedotransfer functions are valuable in hydrologic analysis because they transform readily available measurements into structured data. This work develops a pseudocontinuous pedotransfer function for prediction of cumulative infiltration using Soil Water Infiltration Global (SWIG) database. Ten different datasets were provided as input data of infiltration characteristics. The largest input data has more than 3250 infiltration curves and 80,000 datapoints involving classic and non-classic infiltration curves. This study is focused on the impact of the regularization technique and Artificial Neural Network (ANN)-based ensemble modeling on the accuracy of pedotransfer infiltration functions. To this end, the Multi-Layer Perceptron (MLP) was equipped with a Doublelayer ELastic net regularization technique (i.e., MLP-DEL) for examining input selection (parsimony) and optimizing the geometry (sparsity) of the neural networks. A novel hybrid grey wolf optimizer-trust region algorithm is presented for the MLP-DEL. K-means and a self-organizing map network were embedded within the Bootstrap AGGregatING (Bagging) algorithm to investigate the role of ensemble modeling on infiltration pedotransfer functions. This paper’s results show that the optimal architecture of the single non-regularized ANNs for each of the ten input datasets generally consists of the Bayesian Regularization Backpropagation (BRB) with deep hidden layer and Tansig/Logsig activation functions. The investigation of sparsity-parsimony of the ANN pedotransfer models by MLP-DEL revealed that the regularization algorithm improves the accuracy of the single nonregularized ANN by removing the outliers provided that the ANN is not overly complex. The distribution of regularization factors in the two-stage regularization algorithm implied that while the accuracy of the ANN pedotransfer functions is dependent on all inputs, and the improvement of the accuracy occurs by re-arraignment of the hidden-output weights of the network. It is shown by this paper’s results that bagging ensemble modeling improves the RMSE, MAE and NSE of simulated values by 33%, 45% and 90%, respectively. It is also shown that the ensemble model compensates the weakened based learners such that an improvement of 198% was observed in the NSE of the ensemble bagging model compared to the single model.

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