Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system

Abstract

Summary Modeling of soil water content (SWC) is one of the most studied topics in hydrology due to its essential application to water resources management. In this study, an adaptive neuro fuzzy inference system (ANFIS) method is used to simulate SWC in the extreme arid area. In-situ SWC datasets for soil layers, with depths of 40 cm (layer 1), 60 cm (layer 2) below surface was taken for the present study. The models analyzed different combinations of antecedent SWC values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANFIS models in training and validation sets are compared with the observed data. In layer 1, the model which consists of six antecedent values of SWC, has been selected as the best fit model for SWC modeling. On the other hand, which includes two antecedent values of SWC, has been selected as the best fit model for SWC modeling at layer 2. In order to assess the ability of ANFIS model relative to that of the ANN model, the best fit of ANFIS model of layer 1 and layer 2 structures are also tested by two artificial neural networks (ANN), namely, Levenberg–Marquardt feedforward neural network (ANN-1) and Bayesian regularization feedforward neural network (ANN-2). The comparison was made according to the various statistical measures. A detailed comparison of the overall performance indicated that the ANFIS model performed better than both the ANN-1 and ANN-2 in SWC modeling for the validation data sets in this study.