Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (Ks) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict Ks, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict Ks using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points × 7 factors = 791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R2) in comparison with the initial FIS and the GA-FIS model. EF and R2 values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME = 10.4 versus 11.5 and RMSE = 5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.