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In this study some soil phosphorous sorption parameters (PSPs) by using different machine learning models (Cubist (Cu), random forest (RF), support vector machines (SVM) and Gaussian process regression (GPR)) were predicted. The results showed that using the topographic attributes as the sole auxiliary variables was not adequate for predicting the PSPs. However, remote sensing data and its combination with soil properties were reliably used to predict PSPs (R2 =0.41 for MBC by RF model, R2=0.49 for PBC by Cu model, R2=0.37 for SPR by Cu model, and R2=0.38 for SBC by RF model). The lowest RMSE values were obtained for MBC by RF model, PBC by SVM model, SPR by Cubist model and SBC by RF model. The results also showed that remote sensing data as the easily available datasets could reliably predict PSPs in the given study area. The outcomes of variable importance analysis revealed that among the soil properties cation exchange capacity (CEC) and clay content, and among the remote sensing indices B5/B7, Midindex, Coloration index, Saturation index, and OSAVI were the most imperative factors for predicting PSPs. Further studies are recommended to use other proximally sensed data to improve PSPs prediction to precise decision-making throughout the landscape.

In areas with dry and semi-arid climates, underground water is one of the main sources of water supply for agriculture, industry and drinking. The aim of this research is to investigate hydrodynamic coefficients, plain balance and aquifer thickness in the study area of Kermanshah through aquifer simulation using MODFLOW model. In this method, first the conceptual model and then the numerical model were built in two steady and unsteady states, then it was calibrated and validated. The results show that the maximum thickness of the aquifer in the north and northeast of the plain is about 160.96 meters, and the lowest thickness is 118.14 and 120.69 in the southwest and west of the plain, respectively. Also, the coefficient of hydraulic conductivity varies between 0.09 and 40 m/day and the coefficient of specific yield varies between 1 and 35%. Balance calculations showed the negative water balance in the western and southwestern areas of the plain and the positive balance in the northern, northeastern, and southeastern areas.

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.