Estimating Cumulative Infiltration Using Artificial Neural Networks in Calcareous Soils

AbstractInfiltration process is one of the most important components of the hydrological cycle. On the other hand, the direct measurement of infiltration process is laborious, time consuming and expensive. In this study, the possibility of predicting cumulative infiltration in specific time intervals, using readily available soil data and Artificial Neural Networks (ANNs) was investigated. For this purpose, 210 double ring infiltration data were collected from different regions of Iran. Basic soil properties of the two upper pedogenic layers including initial water content, bulk density, particle-size distributions, organic carbon, gravel content (>2mm size), CaCO3 percent and soil water contents at field capacity and permanent wilting point were determined on each soil sample. The feedforward multilayer perceptron was used for predicting the cumulative infiltration at times 5, 10, 15, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240, 270 minutes after the start of the infiltration test and the time of basic infiltration rate. The developed ANNs were categorized into two groups; type 1 and type 2 ANNs. For developing type 1 ANNs, the basic soil properties of the first upper soil horizon were used as inputs, hierarchically. In developing the type 2 ANNs, the available soil properties of the two upper soil horizons were used as inputs, using principal component analysis technique. Results of Reliability test for developed ANNs indicated that type 1 ANNs with a RMSE of 1.136 to 9.312 cm had the best performance in estimating the cumulative infiltration. Also, type 1 ANNs with the mean RMSD of 6.307 cm had the best performance in estimating the cumulative infiltration curve.