No abstract

Due to numerous droughts in recent years, the amount of surface water in arid and semi-arid regions has decreased significantly, so reliance on groundwater to meet local and regional demands has increased. The Kabgian watershed is a karst watershed in southwestern Iran that provides a significant proportion of drinking and agriculture water supplies in the area. This study identified areas with karst groundwater potential using a combination of machine learning and statistical models, including entropy-SVM-LN, entropy-SVM-SG, and entropy-SVM-RBF. To do this, 384 karst springs were identified and mapped. Sixteen factors that are related to karst potential were identified from a review of the literature, and these were compiled for the study area. The 384 locations were randomly separated into two categories for training (269 location) and validation (115 location) datasets to be used in the modeling process. The ROC curve was used to evaluate the modeling results. The models used, in general, were good at determining the location of karst groundwater potential. The evaluation showed that the E-SVM-RBF model had an area under the curve of 0.92, indicating that it was most accurate estimator of groundwater potential among the ensemble models. Evaluation of the relative importance of each of the 16 factors revealed that land use, a vector ruggedness measure, curvature, and topography roughness index were the most important explainers of the presence of karst groundwater in the study area. It was also found that the factors affecting the presence of karst springs are significantly different from non-karst springs.

Urban tourism has developed into one of the most efficient and effective economic and recreational activities in the modern era since the late second half of the twentieth century. Although attention to urban tourism development is increasing, this paper indicates that no comprehensive study has yet combined the two subjects of tourism development and branding for spatial modeling. Thus, for assessing urban tourism potential (AUTP), a novel hybrid modeling approach combining K-mean, fuzzy logic, and an artificial neural network (ANN) was used. The findings indicate that in Tabriz metropolis, areas 3 and 7 and the southwestern portion of region 6 have the most significant potential for urban tourism. In contrast, areas 4, 8, and 10 located within the worn-out urban fabric have the least potential for tourism. Subsequently, by examining the correlation between urban tourism conditioning factors (UTCFs) and tourism maps, it was determined that factors including distance from the catering centers, distance from the fault, quality of construction materials, distance from the historical centers, distance from the health centers, maximum temperature, and distance from the parking are the most critical in terms of increasing urban tourism's potential and branding. The analyses conducted in this study provide valuable and practical information for developing future strategies urban tourism. To this end, recommendations have been made to enhance tourism destination service delivery and management through branding.