JWSS - Journal of Water and Soil Science

Today, supplying water to meet the sustainable development goals is one of the most important concerns and challenges in most countries. Therefore, identification of the areas with groundwater potential is an important tool for conservation, management and exploitation of water resources. The purpose of this research was to prepare the potential groundwater map in Nahavand, Hamedan Province, using the weight of evidence model and combining it with logistic regression. For this purpose,  the information layers of slope angle, slope aspect, slope length, altitude, plan curvature, profile curvature, TWI, SPI, distance from fault, fault density, distance from river, drainage density, lithology and land use were identified as the  factors affecting groundwater potential and digitized in the ArcGIS software. After designing the groundwater potential map with these three methods, ROCs were used to evaluate the results. Of 273 springs identified in this study, 191 (70%) were used to prepare the groundwater potential map and 82 springs (30%) were used to evaluate the model. The area under curve (AUC) obtained from the ROC curve showed an accuracy of 80.4% for the weight of evidence model and 82.5% for the weight of the evidence- regression combined model

Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.