Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area.

The objective of this research was to determination the effective parameter on landslide occurrence and compare the landslide susceptibility zoning methods including Support Vector Machine (SVM) and Gaussian Process (GP) regression based on two kernels (Pearson VII and radial basis) and Random Forest (RF) in the part of Haraz watershed, Iran. In present research, nine factors like slope, aspect, elevation, geology, land use, distance of fault, distance of road, distance of river and precipitation were used as key parameters for assessment of landslide susceptibility. Three statistical comparison criteria including Nash–Sutcliffe model efficiency (NSE), Coefficient of Correlation (C.C) and Root Mean Square Error (RMSE) were used to determine the best performing model. The obtained results shown that the Rf model (with C.C = 0.9753, RMSE = 0.1434 and NSE = 0.9176) is more accurate to assess the landslide susceptibility as compare to the other models. Sensitivity analysis suggeste that the factor, aspect, plays the most substantial role in the evaluation of landslide susceptibility. Comparison of results displays that there is no important diversity between observed and predicted values of landslide occurrence and landslide non-occurrence using GP_PUK, GP_RBF, SVM_PUK, SVM_RBF and Random Forest approaches.