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Showing 3 results for Salimi
Risk assessment and prioritisation between individual and group tourists at the desert destination (case study: Marnjab Desert)
Iraj Ghasemi, Sheida Ebrahimi Salimi,
Volume 7, Issue 4 (2-2021)
Abstract
Introduction
The development of the tourism industry, in addition to paying attention to the infrastructure of this industry, requires comprehensive planning of persuasive factors, as well as reducing the environmental and natural risks of tourism destinations. According to research, tourists are affected by four types of risks, including health, cultural, political and economic, but among the natural hazards that endanger the health of tourists is of particular importance.
 Among the tourist destinations, ecotourism has a significant success, which causes many hazards in these areas. Maranjab desert for the relative temperament of temperature, tourist attractions, diversity of animal species and vegetation, and the existence of typical and prominent forms of desert is one of the most visited areas of desert ecotourism. Therefore, many problems and dangers are threatening. In this research, an attempt has been made to identify and analyze the main natural and environmental hazards of the Maranjab desert with a descriptive-analytical method based on library and field studies.
methodology
The general approach of mixed-method with the priority of quantitative method is based on qualitative studies. For this purpose, after identifying the risks, a questionnaire for prioritization was collected through interviews with experts and then evaluated and analyzed through the FMEA technique. The method of FMEA is one of the tools for continuous improvement of product and service quality. The purpose of the FMEA is to identify the risks and risks of the product and process that may be latent or obvious. Once identified, the next step is to make decisions that can be addressed. This method is used in medicine, manufacturing and services industries. In recent years, the use of this model for risk assessment in the humanities and tourism has also become popular. This method is based on three key components of probability of occurrence, severity of occurrence and probability of discovery.
 After returning the questionnaires and evaluating the quality of response, a random sample of 100 questionnaires was selected and analyzed based on the method of analysis of failure factors and its effects. According to the purpose of the study, half of the audience had an individual trip and half of them traveled to the area with the group. Audiences were asked to assign a score between 1 and 10 for each component of the method. Accordingly, each factor will have a score in each case, which is obtained from the average score of the audience and has been between 1 and 10. After identifying and evaluating the risk perceived by the audience, in an interview with professors and

Using Machine Learning Algorithms for Modeling Groundwater Resources in Arid Rangeland Western Iran
Nazanin Salimi , Marzban Faramarzi, Dr Mohsen Tavakoli, Dr Hasan Fathizad,
Volume 10, Issue 3 (9-2023)
Abstract
In recent years, groundwater discharge is more than recharge, resulting in a drop-down in groundwater levels. Rangeland and forest are considered the main recharge areas of groundwater, while the most uses of these resources are done in agricultural areas. The main goal of this research is to use machine learning algorithms including random forest and Shannon's entropy function to model groundwater resources in a semi-arid rangeland in western Iran. Therefore, the layers of slope degree, slope aspect, elevation, distance from the fault, the shape of the slope, distance from the waterway, distance from the road, rainfall, lithology, and land use were prepared. After determining the weight of the parameters using Shannon's entropy function and then determining their classes, the final map of the areas with the potential of groundwater resources was modeled from the combination of the weight of the parameters and their classes. In addition, R 3.5.1 software and the randomForest package were used to run the random forest (RF) model. In this research, k-fold cross-validation was used to validate the models. Moreover, the statistical indices of MAE, RMSE, and R2 were used to evaluate the efficiency of the RF model and Shannon's entropy for finding the potential of underground water resources. The results showed that the RF model with accuracy (RMSE: 3.41, MAE: 2.85, R² = 0.825) has higher accuracy than Shannon's entropy model with accuracy (R² = 0.727, RMSE: 4.36, MAE: 3.34). The findings of the random forest model showed that most of the studied area has medium potential (26954.2 ha) and a very small area (205.61 ha) has no groundwater potential. On the other hand, the results of Shannon's entropy model showed that most of the studied area has medium potential (24633.05 ha) and a very small area (1502.1 ha) has no groundwater potential.

Evaluation of soil carbon accumulation potential of Capparis decidua and Prosopis cineraria species (Case study: Keshtegan, Saravan)
Mohammad Reza Rigi, Atefeh Alie Anvari, Farhad Zolfaghari, Khaled Salimi,
Volume 10, Issue 4 (12-2023)
Abstract
Introduction: Nowadays, climate change and global warming caused by increasing concentrations of greenhouse gases, especially carbon dioxide, is one of the major challenges facing sustainable development. Carbon accumulation in plant biomass and soils is the simplest and economically way to reduce levels of this atmospheric gas. No research has been done on the assessment of the potential of soil carbon deposition and accumulation in the Capparis decidua and Prosopis cineraria species.
Data and Methodology: The objective of this study was to evaluate the soil carbon accumulation in Capparis decidua and Prosopis cineraria plant species in Keshtegan of Saravan, Iran. Therefore, in order to determine the amount of carbon stored in the soil, soil sampling was done by random-systematic method. One hundered-meter transects were randomly selected in the study areas and sampling points were dug at similar intervals along the transect for sampling.Soil samples were taken from depth of 0 to 30 centimeters under the canopy of Capparis decidua and Prosopis cineraria and bare soil as control (20 samples from each area). Soil organic carbon, soil bulk density, pH, salinity and content of clay, silt and sand were recorded.
Description and Interpretation of Results: The analysis of the data showed that there is a significant difference between the investigated treatments in terms of the amount of clay, organic carbon and carbon accumulation. The average comparison results showed that there is a significant difference between the soil covered by plant species and the soil of the control area. The amount of soil carbon accumulated in the area covered by Capparis decidua (1.32 tons per hectare) was significantly higher than that in area covered by Prosopis cineraria (0.75 tons per hectare) and the control area (0.25 tons per hectare). It shows the positive effect of two plant species on the amount of soil carbon accumulation. The average amount of organic carbon in the area with the Capparis decidua, Prosopis cineraria and the control area was  0.75, 0.31 and 0.1 tons per hectare, respectively.Soil organic matter and sand percentage under the canopy of both plant species were higher than the control. In terms of other characteristics, no significant difference was observed in the three regions. According to the results, it can be stated that the presence of plant canopy can increases the amount of carbon accumulation in the soil and led to global warming mitigation.
 
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