modeling

Today, energy consumption plays a decisive role in the qualitative and quantitative development of human life. One of the energy sources that aligns with development, economic prosperity, and the acquisition of climatic comfort is natural gas. This energy source, especially in cold regions of the country, is essential for providing thermal comfort and requires proper management. Effective management of this fossil energy source depends on awareness and accurate forecasting of its demand. For this reason, the demand for natural gas in Zanjan city, one of the cold cities in Iran, was studied and modeled. Two groups of data—weather elements and natural gas consumption—over a period of 9 years (2013–2021) on a daily scale were used for this study. CurveExpert software and regression methods were employed to model the demand for natural gas in the city. Based on the most accurate pattern, temperature was selected as the only independent variable in the chosen model. Polynomial regression, with a correlation coefficient of 0.94 (coefficient of determination of 89.03%), was selected as the final model. The analysis revealed that the percentage increase in natural gas consumption per one-degree decrease in temperature varies across different temperature ranges. From 22°C to 16°C, the highest percentage increase in consumption was observed, while from 0°C to -5°C, the lowest percentage increase per one-degree decrease in temperature was recorded. The turning point and the beginning of issues related to natural gas shortages in Zanjan city were identified to occur at temperatures below -7°C.

Urban development strategy represents a contemporary approach in urban planning and management, offering a framework for achieving sustainable urban development through poverty reduction, citizen participation, and increased investment. Like many cities in Iran, Ardabil faces numerous challenges, including inadequate governance, deteriorating infrastructure, substandard housing, weak public utilities, and insufficient social and health services. These issues underscore the necessity of strategic planning to address urban development effectively. This study aims to evaluate the indicators of urban development strategy in Ardabil. Employing a descriptive-analytical methodology, the research is grounded in field investigations. A sample size of 230 participants was determined using Sample Power software. Data analysis was conducted using one-sample T-tests and structural equation modeling (SEM) in AMOS Graphics software. The results of the one-sample T-test revealed that the status of urban development strategy indicators—livability, good governance, bankability, and competitiveness—in Ardabil is suboptimal, with mean scores of 2.21, 2.6, 2.62, and 2.15, respectively. Second-order factor analysis indicated that among the dimensions of urban development strategy, competitiveness, with a factor weight of 0.93, exerts the most significant influence, while bankability, with a factor weight of 0.62, has the least impact. Furthermore, the findings demonstrated that an improvement in the good urban governance index leads to a 0.55 increase in bankability, a 0.76 increase in livability, and a 0.86 increase in competitiveness. These results highlight the interconnectedness of governance quality with other dimensions of urban development, emphasizing the need for integrated strategies to enhance Ardabil's urban sustainability.

Simulation and continuous review of dynamic processes and urban growth patterns with regard to past development and its prediction in the future, for planners and proponents of natural resource conservation in setting sustainable development strategies, achieving sustainable urban development as well as better decision making It is vital to guide future development. In this regard, the city of Karaj with the approach of being the center of the province as being industrial and migrating has had an impact on all the internal structure of the surrounding cities and villages. As a result, the use of hybrid modeling with a deeper and local perspective for codified planning for the sustainable development of the city of Karaj is inevitable. The aim of this study was to model the growth patterns of Karaj city using heuristic statistical preprocessing (ER), ordinary least squares regression (OLS) and spatial weighted regression model (GWR) and also to predict its development using the model. CA-Markov considering the 20-year period 2002-2021.

Methods and Material: 

For this purpose, first, effective criteria in this process were collected, analyzed and prepared from relevant organizations, and land use maps were extracted from Landsat satellite images. In the next step, the maps were validated and changes were detected. The results of change detection show that the largest increase in area occurred in the built-up areas (2893 hectares) and the largest decrease in area occurred in barren lands (808 hectares). Based on these changes and to avoid the trial and error method in selecting the best combination of input criteria to the GWR model, pre-processing was performed on the criteria using the ER and OLS methods. In the next step, considering the output of ER and OLS methods, 8 independent variables were selected as inputs to the models. Then, modeling of urban growth patterns was performed using the GWR model.

results showed that out of 8 variables, two variables had the same direction of impact (negative or positive) in the studied space and the other variables change their impact in the whole region. Finally, using the CA-Markov model, the land use map for 2041 was predicted. The final results showed that the CA-Markov model predicts that the highest rate of development will occur in 2041 in the eastern and northeastern parts of Karaj.

By employing urban growth and development modeling, it is feasible to delineate a developmental trajectory that aligns with the specific circumstances of a city, considering environmental factors, natural elements, and population dynamics. The aim of this research is to propose an urban development model for Shushtar, which can serve as a valuable tool for analyzing the intricate processes of urban transformations. To accomplish this objective, two datasets were utilized: urban land use maps (including educational spaces, healthcare facilities, residential areas, etc.) and Landsat satellite imagery for key land uses such as rivers, barren lands, and forests, spanning three time periods: 1991, 2004, and 2014. These datasets were processed using GIS and MATLAB software. Existing urban land use maps were digitized and subsequently updated using Landsat satellite imagery. Subsequently, influential parameters in urban development were introduced as inputs to the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. After training the model for the years 1991 and 2004, the predicted results of urban development using the algorithm were compared with the actual situation in 2014, demonstrating a high accuracy of 93.7%. The land use change map, resulting from the change detection process, can be generated based on multi-temporal remote sensing images and their integration with urban land use maps, enabling an analysis of the associated consequences. The use of intelligent algorithms in this research has facilitated modeling with a high level of accuracy. The obtained results are deemed acceptable, and this development has also been predicted for the upcoming years.

In areas with special characteristics such as desert areas, the development of sports is one of the most important options for the development of infrastructure as well as the expansion of the economic origins of these areas. These areas can develop and improve the proposed options for regional tourism. For the development of sports in specific areas such as the desert, it is necessary to provide specific cultural and economic plans that will ultimately lead to the regional development of sports as well as the economic development of the region and the satisfaction of tourists. Desert climate regions in Iran are located among one of the largest desertification belts in the world. Iran is one of the most important countries with significant deserts, whose deserts have high tourism and ecotourism potentials. Tourism in the desert is not only one of the main options for the development of desert and desert sports, but it can contribute to the prosperity of that region based on economic factors that include the costs of desert tourism, the amount of investment in this part of tourism, the rate of return on investment and other issues. also help a lot. 2. Research MethodologyThe current research was qualitative and applied in terms of purpose. Since in this research, in terms of the lack of theoretical foundations, it was not possible to gain sufficient knowledge about the important indicators related to the problem and their relationship with each other, the qualitative approach and the foundational grounded-theory method became the basis of the work; Because it is believed that the grounded-theory of the foundation is beyond all descriptive methods due to its conceptualizations of social phenomena. The foundational grounded-theory method is inductive in terms of approach and based on it, the theory is formed from the concepts obtained from the data. In this research, the approach of Strauss and Corbin (1998) was used, because it has a systematic method for data analysis. The statistical population included all experts and specialists related to sports tourism, desert tourism, sustainable development of tourism and topics related to them. Based on this, academic staff members and researchers in the field of sports tourism, senior managers and experts of sports and youth departments and tourism organization of Yazd province were selected as the statistical population. In the current research, the theoretical sampling method was used, which is one of the main features of the foundational groded-theory. This method is a combination of purposeful and snowball sampling methods, which is done by considering the theoretical saturation index compared to sampling from the research population, thus, finally, 22 people from the statistical population were interviewed. The analysis of the collected data was done through three stages of open coding, central coding and selective coding..3. Results and discussionThe open coding stage started at the same time as the end of the first interview. In this way, all the interview transcripts were prepared and examined word by word and this work was done for all the interviews. Then, in the axial coding stage, the data that were broken down in the open coding stage, are classified based on thematic and conceptual similarity, and this work continues until the concepts assigned to each category, a comprehensive concept and be abstract in this stage, one of the categories obtained in the open coding stage is selected as the main category or phenomenon and placed in the center of the process, and then other categories are related to it. In the current research, the central category, sustainable development of sports tourism, was determined with an emphasis on the capacities of the desert. After performing all the steps related to open and axial coding, a total of 396 primary codes were extracted from the transcripts of 22 interviews and their analysis, which by examining their similarities and classification, 127 more abstract concepts and finally through Classification of similar concepts, 29 main categories were identified; All these categories and concepts were divided under 5 general categories of causal conditions, contextual conditions, intervening conditions, strategies and consequences. The causal conditions included employment based on entrepreneurship, socio-cultural factors, emotion management, a global position of sports and global tourism, and economic growth. Concepts and categories related to background conditions included geographical and security conditions, socio-cultural conditions, political conditions, existence of fans and volunteer forces, natural and artificial roads, laws and regulations, and economic and budget problems. Concepts and categories related to intervening conditions included managerial and structural factors, service-facility conditions, human resources, political conditions, media and information systems, and financial and economic conditions. 4. ConclusionToday, tourism has found a global scope and is considered one of the phenomena arising from the all-round development of technological innovations in the modern era. What has turned tourism into a factor in the development process is the flow of capital, culture and information in an overlap with factors such as increased income, level of well-being, more leisure time and improved communication. One of the determining factors in the tourism industry is to pay attention to sports and its development and strengthening, as well as organizing competitions and events at various domestic and international levels. The role of sports in many economic, social and cultural fields is the necessity of investing in this category, and an economic look at sports seems necessary today. Sports tourism takes place in different regions and includes various propositions according to the type of region and the type of regional options; One of the most important and most powerful of these areas are the deserts, which also form an important part of the nature of this country in Iran, and the present research has investigated the issue of sustainable development of sports tourism with emphasis on their capacities.

This research examines spatial poverty in the rural settlements of Zanjan Province through a descriptive-analytical approach. The study investigates the impact of geographical, infrastructural, and socio-economic factors on the spatial distribution of poverty by utilizing official census data, satellite images, and upper-level plans. Data analysis was conducted using spatial statistical methods, including the Ordinary Least Squares (OLS) model, which identifies relationships between independent variables such as elevation, slope, and distance from urban centers, thereby explaining local variations in poverty indices. Furthermore, the Spatial Poverty Degree Index (SRPDI) was employed to evaluate the influence of these factors on spatial poverty, followed by the synthesis of components through the Spatial Poverty Index (SPI) and clustering analysis. The results indicate that over half of the settlements are situated in high-altitude and steep slope areas, leading to limited access to infrastructure and natural resources. Additionally, low literacy rates and a lack of an active population contribute to unfavorable social conditions in these regions. The findings highlight the need for multidimensional strategies and targeted policies to reduce poverty, including improvements in service access, infrastructure development, and social capital strengthening. This research provides a scientific basis for policymaking aimed at sustainable development and reducing regional inequalities by identifying the complex interrelationships among various factors.

Cultivating new development paths in urban areas requires a strategic planning process. Strategic planning is considered as one of the executive steps of futures research through the presence of citizens, their participation, and their awareness raising in different fields. In this sense, futures research is not a substitute for strategic planning; rather its content upon which the strategy is formulated is of importance. This research is applied in terms of its purpose, and descriptive-analytical research in terms of its methodology. By studying the theoretical foundations and using the Delphi method, influential indicators and variables in strategic planning in the form of 4 general dimensions (physical, socio-cultural, institutional management, and structural-process), and 39 items or sub-indices were extracted. The analysis of the data collected in the current research was done using statistical tests, such as cross-effects analysis and structural equations, GWR tool from the spatial statistics collection, etc., using Micmac, Smart PLS, GIS, and SPSS software. The results of modeling the effects of official institutions on the futures research indicated that "transparency of goals, policies, and official and unofficial instructions of the organization" have the greatest contributions to the direct and indirect effectiveness of futures research in Isfahan. Furthermore, the results of the effective spatial analysis on the expansion of strategic planning in the city of Isfahan showed that the managerial-institutional dimension would have significant effects on the expansion of strategic planning in this city, and the socio-cultural index is among the most important indicators influencing strategic planning at the level of this city.

When the role and function of the customer are properly defined in the culture of a society and customer service is considered and acted upon as a value, organizations are placed in their correct orbit and circle. One of the fundamental challenges of Bank Sepah is the lack of a comprehensive redundancy modeling framework for its banking services. Therefore, this study aims to address these challenges by developing a comprehensive redundancy modeling framework specifically designed for Bank Sepah's banking services. The present study was conducted in two qualitative and quantitative parts. Also, library studies, including studying domestic and foreign books and publications and searching databases (Internet), were used to obtain theoretical foundations and use the experiences of other researchers to collect data. The results of the study show that the redundancy of banking services includes improving the quality of services, increasing access to online services, and improving the customer experience. Finally, the redundancy of banking services can act as a key factor in the success and sustainability of banks in competitive markets.

Urban weathering refers to the processes of loosening, decay, and eventual deterioration of materials used in various urban constructions. This study focuses on assessing the degree of weathering in gravestones from the Joy-e-Horhor and Khold-e-Barin cemeteries in Yazd. A combination of petrographic analyses and longitudinal monitoring of Schmidt hammer rebound values for hundreds of gravestones was employed to achieve this. The findings indicate that in addition to the petrographic characteristics of the stones, such as mineralogical composition, mineral diversity, and rock texture and fabric, local climatic conditions significantly influence the weathering and degradation of these materials. Key processes contributing to the loss of stone durability include temperature fluctuations leading to thermal expansion and contraction, the albedo effect of the stone, wet-dry cycling, and the crystallization and dissolution of secondary minerals like calcite and gypsum. Gravestones made of travertine and marble, characterized by a predominance of calcite minerals and light-colored surfaces, exhibit higher resistance to weathering compared to other lithologies, provided they are not exposed to excessive moisture or frequent washing. In contrast, low-grade metamorphic rocks such as slate and phyllite are the least suitable for gravestones due to their high density of fractures and cleavage planes. Similarly, dark-colored igneous rocks are prone to rapid durability loss, as the differential thermal expansion and contraction of their constituent minerals in response to temperature changes accelerate their weathering processes.

Dry Spells are a defined as a sequence of days without rain. It is very important to evaluate and determine the efficiency of statistical models to estimate the start and end of dry periods and it is widely used in research related to different sectors such as water resources, agriculture, watershed management. Therefore, it is very important to identify the most appropriate models for displaying rainfall distribution to determine the beginning and end of dry periods, because it can be used in various applications such as water resources management, agricultural planning and hydrological departments. The main and important aim of this research is to modelling behavior of dry Spells and determine the appropriate model to evaluation of periods.

At first, 12 rain gauge stations with a period of 1991-2020 were used to analyze precipitation and extract dry spells lasting 15 days or more. The threshold of 5 mm was used as the final threshold in the analysis because the explanation of more than 50% of the weight of the period. Using the IF function in MATLAB software, dry sequences were calculated for the two criteria of maximum and average length of periods. In the next and important step of this research, in order to determine the efficiency and Performance of the best model in fitting and estimating dry periods, it was necessary to examine the probability distribution and relationships of the models. for weight the models we used, SWARA method.

The climatic behavior of dry spells based on the past and present situation showed that in the future, the average dry spells will increase from east to west and from north to south. The results showed that the average annual dry spells change between 7 and 9 days and the deviation from their average change between 9 and 12 days. In general, the decreasing trend of dry spells has a steeper slope in the direction from north to south. The negative binomial distribution has the most weight in the estimation of the eastern and northeastern regions of the province (on average 87% of the weight of courses in each station) and the combined Markov distribution model of order 1 to 10 has the most weight in the tropical regions of the south and southwest of the province (83% of the weight of the courses). And also the distribution of Weibull fit explained 91% of the weight of the courses for the central regions of the province. In the province as a whole, the negative binomial distribution between observed and estimated values has explained a good weight.

The results of this research, based on several statistical methods and their weighting, showed that the behavior of statistical models in estimating the length of dry spells is strongly influenced by the type of climate of the regions and topographical conditions related to it. The combined fitting of the results of the models showed that for proper and accurate evaluation of dry periods, using the results of several models and then their final weighting can provide a correct explanation of the behavior of the periods. In most of the studies about dry spells, weighting of models has not been considered. In Kohgilouyeh and Boyer Ahmad provinces, due to the different topographical conditions and uneven rainfall in the provinces, the length of long-term dry spells, especially in the central, southern and western dry regions, is a function of the rainfall weight and its behavioral fluctuations. Determining the weight of course criteria such as; The length of periods, probability of occurrence and reversibility indicate that the border of dry areas will be vaster and more northerly in the future climate of the province. In most of the studies about dry Spells, weighting of models has not been considered. In Kohgiluyeh and Boyer Ahmad province, due to the different topographical conditions and uneven rainfall in the provinces, the length of long-term dry spells, especially in the central, southern and western dry regions, is a function of the rainfall weight and its behavioral fluctuations. Determining the weight of course criteria such as; The length of spells, probability of occurrence and reversibility indicate that the border of dry areas will be extended and more northerly in the future climate of the province.

Examining the snow cover and recognizing the patterns of snow formation can inform the environmental planners about the behavior of this phenomenon. In this research, by calling MODIS satellite data in Google Earth Engine environment and using NDSI index, the snow cover condition of Khalkhal city in 2003-2022 was investigated. In order to model snow systems in Khalkhal city, data related to heavy snow report was received from Iran Meteorological Organization. Then, two general patterns for the snow systems of Khalkhal city were determined by the cluster analysis method on the sea level pressure values ​​on the days with snowfall. Then, by selecting a representative day from each model, the situation of sea level pressure, geopotential height, temperature, temperature wind, humidity drift, wind flow and wind speed were analyzed. The results showed that the snow deposits in the central and northern areas of Khalkhal city reach their maximum extent especially in the winter seasons. The results also showed that there are two general patterns for the transfer of cold air mass and snowfall to the study area, each of which has its own mechanisms. The first pattern is influenced by the gradient of pressure and western winds, while the second pattern is influenced by Siberian high pressure systems, air subsidence and northern winds.

The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable and environmentally friendly method to quickly identify and investigate the distribution of heavy metals in soil and thus identify suspected contaminated areas (Scheuber & Köhl, 2003:33). Remote sensing is one of the ways that can provide a cost-effective and quick solution to investigate the distribution of heavy metals on a large scale using spectroscopic techniques (Bi et al., 2009:16). Habibi et al. (2023:4) also measured and evaluated the concentration of heavy metals in the aerial parts and soil of the tree species of Bandar Abbas city and also identified the species that has the highest potential for absorbing heavy metals. The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn>Zn>Pb>Cd. (Nikolaevich, 2023:30) they addressed the modeling of heavy metal pollution in Central Russia based on satellite images and machine learning. Al, Fe, and Sb contamination were predicted for 3000 and 12100 grid nodes in an area of 500 km2 for the Central Russian region for 2019 and 2020. Estimating the amount of this pollution requires time and high cost. Considering the traffic on the Aleshtar -Khorramabad highway near Kakareza forests and the effect of heavy metal concentration in the soil and leaves of the oak species which can be caused by natural and human pollution, the accumulation of heavy metals in the species Iranian oak is a serious threat to this forest. Therefore, it is necessary to study and discuss pollutants and their effects on the environmental cycle. In this regard, considering the cost and time-consuming nature of traditional methods and since remote sensing methods are a suitable complement to traditional methods; the aim of the present research is to use remote sensing techniques and spectral analyses to evaluate and model the accumulation of heavy metals in Iranian oak species.

The present study is located on the road of Aleshtar -Khorramabad, 20 kilometers northwest of Khorramabad. For this purpose, five transects were created at distances adjacent to the road, 500 and 1000 meters on both sides of the road, and 10 x 10 m sample pieces were planted. Inside the sample plots, 30 soil samples were randomly collected and 30 leaf samples were collected from trees in all directions of the crown. To extract heavy metals from soil samples and plant samples, the acid digestion method was used and the physical characteristics of the soil were measured using standard methods. After preparing the samples, the concentration of Pb, Cu, and zinc heavy metals in soil and leaves was measured and the index of biological concentration of heavy metals from soil to leaves was calculated. Then the relationship between the concentration of heavy elements measured and the reflectance in different bands or band ratios at the corresponding sampling points was obtained. Non-parametric methods and generalized multiple linear regression models were used in order to model quantitative variables and spectral values corresponding to sample parts in satellite data. ArcGIS software was used to implement sample parts on the image, ENVI software was used for image processing, and STATISTICA software was used for modeling.

Cu and Pb in Iranian oak leaves had significant differences at different distances at the 0.05 level, but Cu did not have significant differences at different distances at the 0.05 level. Cu and Pb did not have significant differences in different soil intervals at the 0.05 level, but Cu had significant differences in different soil intervals at the 0.05 level. The bioconcentration factor was obtained as (0.2, 0.5, 0.2) mg/kg. The study of modeling of non-parametric methods using Sentinel-2 satellite data showed that the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for the three metals Cu, Pb, and Cu, respectively. The artificial neural network (ANN) algorithm obtained the highest accuracy. Also, according to the results of the random forest algorithm, for the three mentioned metals, PSRI, HMSSI, and PSRI indices are the most important in modeling.Based on the findings, the concentration values of Cu and zinc were significantly different at different distances, but the Cu values were not significantly different at different distances. In this regard, Mansour concluded in 2014 that there is a significant difference between the concentration of Cu and zinc in the leaves of the species, which can be attributed to traffic density and human activities, and the high amount of zinc metal in this study is the wear of car tires؛ and stated that the concentration of Cu is caused by the production of greenhouse gases and the use of vehicles using Cu gasoline. Based on the findings, the values of Cu and zinc concentrations at different distances did not have significant differences, but the Cu values had significant differences at different distances. Sources of input of Cu element to the soil are urban, industrial, and agricultural waste, fertilizers, and chemicals that add it to the soil through liquid, solid, or mineral fertilizers. These findings are with the results of some researchers including Wu and colleagues (2010:38), Botsou et al. (2016:17) are consistent. Based on the findings obtained from the calculation of the bioconcentration index and their comparison with the classification proposed by Ma et al. (2001:25) for Iranian oak species plants in relation to Cu, zinc, and Cu metals from soil to leaves, it acts as an accumulating plant. In accordance with the results of this research, in the study of Khodakarmi et al. (2009:15), Iranian oak was included in the category of superabsorbent plants in relation to the accumulation of Cu pollutants, which has a high capacity in terms of root absorption. Also, Madejón et al. (2006:25) stated that oak leaves are more resistant than olive leaves. The concentrations of elements in leaves and fruits decrease with time and the risk of toxicity in the food web is reduced. The review and comparison of five algorithms showed that (ANN) the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for three metals, Cu, Zn, and Cu, respectively. Considering the importance of the PSRI synthetic band in increasing the accuracy of modeling with satellite images and the influence of the visible and near-infrared bands, the amount of reflection measured by the spectroscopic method showed that with the increase in the concentration of heavy elements, the amount of reflection in the visible and infrared range decreases (Liu et al., 2011:24).

The results showed that Sentinel-2 images along with artificial intelligence techniques have a relatively good ability to model the level of biological pollution index in the region. In line with the obtained results, it is suggested that the Iranian oak species is used to reduce pollution on highways because it accumulates heavy metals.

Cold and frost are one of the climatic hazards that cause damage to various activities every year. Climate change, on the other hand, causes spatial and temporal changes in glaciation. The purpose of this study is to analyze the temporal-spatial changes and predict the future of glaciers in Hamadan province. CanESM2 model was used to predict the minimum daily temperature in the province. Data mining of general circulation models was Downscaling using LARS-WG model. The above parameters were simulated for a period of 30 years (2050-2021) under three scenarios RCP2.6, RCP4.5 and RCP8.5 for selected stations. The results of the monthly minimum temperature survey in the study stations of the province showed that the minimum temperature in the period (2050-2021) in all studied stations according to all three scenarios will increase in all months of the year compared to the base period. The average minimum temperature of the province is equal to 2.5 degrees Celsius, which in the coming decades based on the scenarios of RCP2.6, RCP4.5 and RCP8.5 will reach 6, 6.2 and 6.3 degrees Celsius, respectively, which is the highest The changes are related to Nojeh station and the lowest is related to Hamedan. The spatial distribution of the beginning and end of freezing in the future period indicates that freezing in the northeastern and northern parts of the province starts earlier and ends later than in other parts of the province, while in the southern parts of the province it starts later and ends earlier. The results of examining the changes in the onset of frost in the next decade compared to the base period showed that in all stations studied the onset of frost will decrease between 3 to 11 days.

Monitoring the changes and fluctuations of precipitation in geographical areas can give a better view of the behavior of this phenomenon in the coming years. The purpose of this research is to investigate the precipitation situation in Ardabil Plain (Ardabil, Bileh-Daragh, and Kolour stations) and forecast it in the coming years based on the output of CMIP6 models by the CMhyd downscaling model. Then, using R2, MAE, MSE, RMSE, and Taylor diagram, the observational data of the base period were compared with the historical data of 5 GCM models from CMIP6, and the best model was selected for each studied station. The output of the top models was corrected for skewness by linear scaling method and based on SSP126, SSP245, and SSP585 scenarios, the precipitation of 2050-2023 for each station, forecast, and its trend were drawn with the Mann-Kendall statistic. The results showed that in the eastern and western areas of Ardabil Plain (leading to the heights of Talesh and Sablan mountains), the rainfall changes were increasing (2.80 mm). In the Ardabil station, the MIROC6 model with a correlation coefficient of 0.94%, and in Bileh-Daragh and Kolour stations, the MPI-ESM1-2-HR model with a correlation coefficient of 0.88% and 0.92%, respectively, have the highest accuracy in simulating the precipitation. Also, the results of the scenarios showed that the precipitation changes in Ardabil station in the future period compared to the base period under the SSP126, SSP245, and SSP585 scenarios will be 0.24, -6.36, and -2%, respectively.

In this research, in order to reveal possible climate changes, the time series of annual average temperature of Urmia synoptic station during the statistical period of 1951-2017 was studied. Box-Jenkins prediction model (integrated auto-regressive and moving average model) was used to find the change model and predict the behavior of the time series. After fitting different models, R2, Akaike information criterion (AIC), regression standard error (SEE) and the Schwartz-Bayesin criterion (BIC) were measured to select the optimal model. The results showed that the time series model ARIMA (2,2) (1,0) has better results compared to other models and the trend of time series changes of this model has less error. After choosing the best model, the average annual temperature values for the next 13 years were predicted using it. The temperature prediction was done using this model with a good fit and the correlation between the actual and fitted values was around 82%. After ensuring the correctness of the forecast, the time series until 2030 was estimated. The forecast of the model indicates a mild temperature increase in the coming years with an upward linear trend. The study of the general circulation of the atmosphere of the region by drawing and analyzing the difference maps for the parameters of geopotential height of 500 hpa, relative rotation of 500 hpa, vertical velocity of the atmosphere (Omega) and air temperature on the surface of the earth indicates the strengthening of the stability of the atmosphere as well as the increase in temperature. The air of Urmia city is 0.4 degrees Celsius compared to the past, which confirms the results of the Box-Jenkins model.

Increasing population and urban development, increasing use of cars, increasing the number of private cars, Also the narrow width of the streets and the lack of supply of marginal park space, especially in the central parts, have caused many problems for large and densely populated cities. Parking lots as one of the most important urban infrastructures play a major role in reducing these problems. Today, in large cities, with the correct location of public parking lots, optimally manage of urban traffic is possible by increasing the provision of services to a large number of vehicles. 

In this research, first, effective criteria were extracted according to the opinion of Babol Municipality experts. The information layers of each criterion were prepared in the GIS environment and to equalize the layers, each criterion was classified, Then, the identified criteria were weighted using ANP technique and with the help of Super Decision software, and then pairwise comparisons were done. Weighted layers were combined by ANP method, were placed on top of each other in the GIS environment based on the influence of each layer (relative weight) and the map of the optimal areas for the construction of public parking lots was obtained. 

In this research, the inconsistency coefficient obtained was 0.07. Criteria prioritization showed that, "Distance from the road", "Distance from offices use" and "Distance from business use" criteria with a significance coefficient of 0.25, 0.24 and 0.15 respectively had the highest weights. Arc GIS software was used to prepare the final maps. Finally, suitable places for creating public parking lots were determined by applying the final weight of the criteria and overlapping the layers. According to the final location map, areas with very high potential, areas with high capability and areas with medium capability each have 3.11, 55.75 and 36.56 percent of urban lands, respectively. Areas with very high and high potential are mostly located in the northwestern, northeastern, southwestern and central parts of the city.            

The results indicate that the spatial distribution of existing parking lots in the city is not related to the effective criteria in these parking lots. According to the residential use map and population density and due to the epidemic of private car use, there is no match between the number of parking spaces in the city and their spatial distribution and population density. The result of urban parking location zoning using the ANP model and comparing it with existing parking lots in the city shows that The class of areas with high capacity for parking lots is located in parts of the city where there are no parking areas and there is an urgent need to create these types of users. This factor indicates that the location of existing parking lots in Babol city has been done without considering effective factors and criteria. All parking lots are located in one part of the city and at a close distance from each other. There is a need to create many public parking lots in other parts of the city, according to the urban population, until the per capita is closer to the reality. Considering the population of Babol city, which is 250,217 people, and the number of public parking lots, which is 6, this amount per capita is very low.

Paying attention to the changes resulting from the globalization of the urbanization phenomenon is undeniable in order to face the challenges in the field of urban health. Urban life and the resulting tensions, chronic and contagious diseases, natural and artificial injuries, affect the physical, emotional and mental performance of citizens; Isolation and the stress caused by it put the ability of the residents under the radius. urban health will work to improve the mentioned cases. The present study was developed with the aim of providing a conceptual framework for measuring urban health using qualitative methods. This research examines the macro theories supporting urban health, examines conceptual models for understanding and evaluating urban health at various scales, identifying and extracting indicators affecting urban health in a general sense in the world by benefiting from documentary and library sources and combining theoretical and experimental bases according to the previous stages. And finally, the presentation and design of the conceptual model of urban health assessment has been discussed. The findings from the studies of the last four years show that urban health has been generalized in cities more than any other scale, and the researchers emphasize the subjective studies obtained from it. Also, the examination of all types of health in health studies indicates a multi-way relationship between all the components affecting health. Factors and dimensions affecting urban health in different periods of time and according to the structure, problems and leading challenges of cities are different and are constantly changing.

The main aim of this research was to model the drivers of air pollution in Tehran using the Interpretive-Structural Modeling approach. For this, drivers causing air pollution in Tehran were determined by referring to experts' opinions and using the interpretative structural approach. Then, the influence and dependence of the drivers were assessed using the MICMAC Model. According to the results, the general policies of the government at seventh level located at the lowest level, followed by government interventions on the sixth level, the officials supporting the environment and research and development on the fifth level, innovations in urban management on the fourth level, monitoring the production of pollutants and population growth in the third level, reforming the consumption pattern on the second level, and finally, the number of vehicles, energy price, quality of industries, and clean energy investment were placed at the one level. This research used a structural-interpretive approach, a soft research method in operations, to understand and structure the problem and answer the research question. Furthermore, influential and dependent driving forces were identified so that any change in influential driving forces would lead to changes in dependent driving forces, and the tangible effects of changes on dependent driving forces would be demonstrated. These drivers were used to solve the air pollution problem and pave the way to reaching favorable air quality conditions in Tehran.

The physical development of cities is increasing day by day. Correct management of this development from various aspects is among the important issues that must be considered. There are many methods for predicting and determining the direction of urban development, one of these methods for determining suitable areas is the method based on neural networks.

The purpose of this research is to model the development of the city of Rasht in the last 20 years and predict the directions of development of this city until 2032.

By using ETM+ Landsat 7 and 8 satellite images of 2002, 2012 and 2021 of Rasht city and with GIS software, images with suitable band composition are prepared and then the images are classified using Multi Layer Perceptron(MLP) Artificial Neural Network  method. The indicators considered for the neighborhood model of urban areas are the distance from urban points, the distance to the central areas of the city, and the distance to the main streets and roads.

Studied Areas: 

Rasht city, the capital of Gilan province, is located at 49 degrees 35 minutes 45 seconds east longitude and 37 degrees 16 minutes 30 seconds north latitude from the Greenwich meridian, and its area is about 10,240 hectares.

Results:

 In this model, in the training mode of the first stage (input, applying 4 indexes on the images of 2002), the network performed 104 iterations, and the lowest error rate, which is evaluated by the crossentropy criterion, was equal to 0.058526 in the 98th iteration. In the second step, the input of the model was to apply 4 indicators on the images of 2012, and the lowest error rate was evaluated as 0.076657.

In total, the model has been able to predict the development of Rasht city in 2012, 95.9% and for 2021, 93.8%, which can be acceptable. The model error in this first part was 1.4% and in the second part was 2.6%. By examining the 20-year period of physical development, the development directions of Rasht city in 2032 were predicted.