Showing 727 results for Type of Study: Research
Miss Sorayya Derikvand, Dr Behrooz Nasiri, Dr Hooshang Ghaemi, Dr Mostafa Karampoor, Dr Mohammad Moradi,
Volume 26, Issue 81 (6-2026)
Abstract
sudden stratospheric warming has an obvious effect on the Earth's surface climate. In this research, the changes in precipitation during the occurrence of this phenomenon have been investigated. For this purpose, after revealing the warmings that occurred during the studied period (1986-2020), 18 warmings were identified. The 5th decile and 9th decile of precipitation were calculated for the precipitation data of 117 stations. And the size of the difference from the normal rainfall was checked in two ways. First, the precipitation at the time of warming was compared with the long-term average, and then the trend of changes in precipitation at three times before thewarming, at the same time as the warming, and after the warming was finished. Finally, these results were obtained. Warmings according to the month in which they occur; They have a different effect on the amount of precipitation. In the sudden stratospheric warming that occurred in December, January and February, the northwest experiences the most rainfall changes and is above normal, and the probability of rainfall above the 9th decile increases up to 65%. Western and southwestern regions also have higher than average rainfall and the probability of heavy rainfall is high. Precipitation on the shores of the Caspian Sea shows an inverse relationship with sudden stratospheric warming, so in all the investigations of this research, the lack of precipitation at the time of warming in these areas is significant. Southern regions have less than normal rainfall in all sudden stratospheric warming events. The center of Iran has higher than average rainfall in the sudden stratospheric warming months of March. Eastern Iran also has heavy rains compared to normal during the sudden stratospheric warming months of March.
Dr. Mostafa Kabolizadeh, Dr. Sajad Zareie, Mr. Mohammad Foroughi Rad,
Volume 26, Issue 81 (6-2026)
Abstract
There are various indicators to monitor and management of agricultural water resources in arid and semi-arid countries including Iran, some of which can be extracted directly in situ, and some can be retrieved using remote sensing technology and satellite images. Aim of this study is to propose the most appropriate and efficient indicators of agricultural water resource management for achieving maximum production and maximum water efficiency using remote sensing technology, therefore, Crop Water Stress Index (CWSI) and Surface Energy Balance Algorithm (SEBAL) were used to estimate Evapotranspiration (ET). In the first step, ET rate was calculated using SEBAL algorithm for six Landsat 8 satellite images related to the wheat growth period. Then, zoning of this index was done in the range of zero to one, in four categories of very low, low, medium and high, which respectively indicate the lowest to the highest amount of ET. In next step, CWSI was calculated based on Idso equation, and its results show different changes both in cold season and in warm months. Comparison of ET and CWSI shows a significant relationship between these two indices in warm months, while in cold months, no significant relationship can be seen. These findings along with the established relationship between ET and CWSI can inform water management strategies in arid environments for sustainable crop production.
Professor Keramat Ollah Ziari, Mr Amin Mahmoudiazar, Mr Khalil Jangjoo, Leila Aslani,
Volume 26, Issue 81 (6-2026)
Abstract
One of the issues raised in the developing countries of the world is the issue of reducing damages caused by natural and man-made hazards. Various theories and paradigms such as crisis management and resilience have been proposed to reduce the level of risk vulnerability. Among the mentioned vulnerabilities is physical vulnerability. Now, the most important question that is raised in this research is whether according to the various studies that have been done in the field of physical vulnerability, apart from objective measures, the level of satisfaction of the residents has also been examined and whether it exists. Is there a relationship between objective and subjective indicators in this field? The case study studied in this research is Region 4 of Urmia City, which has a problematic context. This research is of an applied type and its method is descriptive-analytical, to examine this objective and subjective relationship of physical vulnerability, first objective variables were examined using spatial analysis and then subjective variables were examined using a Likert scale. questionnaire. And finally, this relationship has been measured using Pearson's correlation coefficient. The research results indicate that according to the correlation coefficient of 0.623 between subjective and objective variables; There is a significant relationship between the objective view (reality on the ground) and the subjective view (satisfaction of residents) in the field of urban physical vulnerability indicators.
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Dr. Roghayeh Jahdi,
Volume 26, Issue 81 (6-2026)
Abstract
Every year, wildfires burn large areas in the Hyrcanian forests, of northern Iran. This study aims to know the fire regime and assess fire risk in protected areas in Guilan province (256,488 hectar). Fire ignitions and frequency/frequency of burned areas from 1992 to 2022 were identified. Then fire behavior modeling was done to simulate burn probability and fire intensity (i.e. conditional flame length) using the FlamMap modeling system based on fire weather information, topography maps, local fuel models, and historical fire data. By combining maps of simulated burn probability and conditional flame length, a fire hazard map was prepared in the protected areas. According to the obtained results, 8% of the number of historical fires in the period occurred in the protected areas, although most of these fires have very small sizes and limited burned areas (including 0.1% of the burned areas in the province). Frequent fires (fire frequency more than 1) cover 60% of the protected areas, and 11% of these areas are highly likely to ignite. The changes in the burn probability and fire intensity reflect the diversity of fire activity in the protected areas, especially in the south-central parts, which catch the highest values of burn probability (more than 1) and conditional flame length (more than 3 meters). Finally, the fire hazard mapping showed that 77.7% and 4.8% of the protected areas are classified as very low and low fire hazards, respectively. On the other hand, 12.4% and 5.2% of these areas were placed in high and very high hazard classes, respectively. The quantitative results of this research provide scientific criteria for identifying high-priority areas in protected areas where management efforts can help reverse the increasing fire risk of protected forests.
Ms Atefeh Bosak, Dr Zahra Hejazizadeh, Dr Akbar Heydari Tashekaboud,
Volume 26, Issue 81 (6-2026)
Abstract
Air pollution has significant impacts on human health, environmental quality, and the sustainable development of cities. This study aimed to evaluate PM10 using meteorological data from the city of Ahvaz through statistical methods and artificial neural networks. Daily meteorological data and air quality control station data for 4485 days (from 2011 to 2023) were obtained from the National Meteorological Organization and the Khuzestan Department of Environment. Initially, the data were processed and refined, and their normality was assessed using the Kolmogorov-Smirnov test. Given the non-normality of the data, Spearman's and Kendall's Tau-b methods were employed to examine their correlations. The time series and statistical information of the data were obtained using Python programming language. Furthermore, to predict future PM10 levels, the Multilayer Perceptron (MLP) neural network method was utilized. The results of these analyses indicated a significant correlation between meteorological variables and PM10. The Spearman and Kendall Tau-b correlations showed that PM10 had a positive and significant correlation with wind speed (0.094 and 0.061) and temperature (0.284 and 0.187) at a 99% confidence level. Conversely, PM10 exhibited a negative and significant correlation with visibility (-0.408 and -0.300), wind direction (-0.048 and -0.034), precipitation (-0.159 and -0.125), and relative humidity (-0.259 and -0.173) at the 99% confidence level. For future PM10 predictions, the MLP neural network was used. The model was of the Sequential type with an input layer consisting of 6 neurons, three hidden layers of Dense type with 16, 32, and 64 neurons, and an output layer with a linear activation function. The mean squared error (MSE) for the training set was 0.0034, and for the validation data, it was 0.0012. For the test set, the obtained validation accuracy was mse_mlp=0.0048 and val_loss=0.0012. The results indicate a significant direct or inverse correlation between meteorological data and PM10. Additionally, the outcomes of the MLP neural network demonstrated that the network provided satisfactory performance and acceptable predictions for PM10 data in Ahvaz.
Dr Mohsen Ahadnejad Reveshty, Dr Hossein Tahmasbi Moghadam, Dr Ameneh Alibakhshi,
Volume 26, Issue 81 (6-2026)
Abstract
Land use planning is one of the essential aspects of sustainable urban development, aiming to balance land use in urban areas. This study seeks to identify the factors influencing the realizability of service land uses in Zanjan city through a futures studies approach. Data collection employed a combination of library and field methods. In the field phase, the Delphi method was used, engaging 35 experts in urban planning, urban management, and housing, who assessed key factors across two rounds of questionnaires.
A total of 36 factors were identified across five dimensions: legal, economic, socio-cultural, physical-spatial, and managerial. The data were analyzed using MICMAC software. The results indicated that "urban land use laws and regulations" and "service location and spatial distribution" scored the highest direct influence values (85 and 82, respectively), playing the most significant roles in realizing service land uses. Key barriers identified include weak institutional coordination, inappropriate physical development policies, and lack of effective citizen participation. Cross-impact matrix analysis revealed a 55.32% fill rate, indicating a system of interdependent and mutually influential factors that contribute to the instability of service land use realizability. The study proposed solutions to improve the current situation, including: Revising urban laws and regulations, Strengthening institutional coordination among relevant bodies, Utilizing modern technologies such as GIS for proper service location planning, and Enhancing citizen participation culture in urban planning. The findings not only identified key influential factors but also emphasized the importance of considering multidimensional and sustainable aspects in service land use planning. This research provides a foundation for sustainable development and spatial justice in Zanjan city.
Dr Saeedeh Fakhari,
Volume 26, Issue 81 (6-2026)
Abstract
Tehran’s District 12, as one of the capital’s cultural and tourism hubs, hosts a collection of prominent cultural institutions and museums that serve as major attractions for domestic and international visitors. However, the absence of systematic planning for routing between these centers leads to wasted time and energy for tourists and diminishes the quality of their visitation experience. This study aims to optimize museum visitation routes in Tehran’s District 12, focusing on minimizing travel time and distance, by selecting 22 active and significant museums in the area as case studies. To achieve this, the mathematical model of the Open Traveling Salesman Problem (Open TSP) was applied within the framework of network analysis in a Geographic Information System (GIS) environment. Precise spatial data—including the geographic locations of museums and the local street network—were imported into ArcGIS software and processed using the Network Analyst tool. Travel cost matrices (based on time and distance) between all museum pairs were calculated, and optimal visitation routes were extracted and ranked using heuristic Open TSP algorithms according to the criteria of minimum time and shortest distance. Findings indicate that applying the Open TSP model within network analysis leads to the identification of significantly more efficient routes compared to conventional patterns or unplanned visits. Quantitative results show that, under normal (non-optimized) conditions, visiting all 22 museums covers a distance of 25.91 km with a travel time of 310 minutes, whereas the optimized proposed route requires only 9.896 km and 118 minutes of travel time. This improvement represents a 62% reduction in both distance and travel time. The study demonstrates the high efficiency of integrating combinatorial optimization models with GIS spatial analysis capabilities for urban tourism planning and can serve as a model for intelligent management of tourist visitation routes in other urban areas. The results enable informed decision-making and optimal planning for both group and individual visits, significantly enhancing the tourism experience by reducing time and physical costs.
