Predicting the infiltration characteristics for semi-arid regions using regression trees

The study of the infiltration process is considered essential and necessary for all hydrology studies. Therefore, accurate predictions of infiltration characteristics are required to understand the behavior of the subsurface flow of water through the soil surface. The aim of the current study is to simulate and improve the prediction accuracy of the infiltration rate and cumulative infiltration of soil using regression tree methods. Experimental data recorded with a double ring infiltrometer for 17 different sites are used in this study. Three regression tree methods: random tree, random forest (RF) and M5 tree, are employed to model the infiltration characteristics using basic soil characteristics. The performance of the modelling approaches is compared in predicting the infiltration rate as well as cumulative infiltration, and the obtained results suggest that the performance of the RF model is better than the other applied models with coefficient of determination (R-2) = 0.97 and 0.97, root mean square error (RMSE) = 8.10 and 6.96 and mean absolute error (MAE) = 5.74 and 4.44 for infiltration rate and cumulative infiltration respectively. The RF model is used to represent the infiltration characteristics of the study area. Moreover, parametric sensitivity is adopted to study the significance of each input parameter in estimating the infiltration process. The results suggest that time (t) is the most influencing parameter in predicting the infiltration process using this data set.