Showing 20 results for Regression
Mohammad Radman, Mohammad Saligheh, Mohammad Hossein Naserzadeh,
Volume 0, Issue 0 (3-1921)
Abstract
In order to comprehend the water flow characteristics and variations of the Karun River, we examined the Zaz, Bazoft, and Beshar sub-basins from its main branches. The reason for choosing these basins was the proximity to the catchment centers of the Middle Zagros and their location upstream of the dams.
Iran Water Resources Management Company provided all the required data (from the water year
1356-57 to 1395-96), and we analyzed them using Kolmogorov-Smirnov tests, data skewness, skewness, and Pearson correlation. Then, we performed the linear regression test to determine the effect of temperature and precipitation on river discharge, and they conducted the Mann-Kendall test to identify the trend and jump points. The results of the data analysis showed that all of them are in normal conditions, although they have some elongation and skewness. The Pearson correlation test revealed a correlation between meteorological and hydrometric data.
The regression model used shows the changes in precipitation and discharge (unlike temperature and discharge) well. The significance number of all stations in the model is less than 0.05, which shows that the changes that occurred between predictor and dependent variables are significant. We see the high performance of the model in explaining the changes in discharge compared to precipitation. According to the regression charts, the decreasing trend of precipitation and discharge and increasing temperature are clear in all three basins.
The Mann-Kendall test reveals a significant trend of increasing temperature in Bazeft and Bashar basins, a decreasing trend of discharge in Bazeft and Bashar basins, and a decreasing trend of precipitation in Zaz and Bazoft basins.except for the temperature of the Zaz basin, all variables show mutations in mutation basins.
Ms Atefeh Bosak, Dr Zahra Hejazizadeh, Dr Akbar Heydari Tashekaboud,
Volume 0, Issue 0 (3-1921)
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.
Hossein Asakereh, Mehdi Dostkamian,
Volume 15, Issue 36 (6-2015)
Abstract
All the water vapor of atmosphere is contained in a column of the atmosphere that is capable of precipitation and it is from the ground to the final of water vapor called perceptible water. This element influenced by topography and height. The purpose of this study is survey about impact of local and spatial factors on distribution of perceptible water maximums in Iran.For this reason, pressure data, especially moisture, orbital and meridional components extracted from NCEP/NCAR and analysis. Correlation and regression methods were used in this study. In order to better survey about perceptible water gradient changes and gradient changes of maximum of perceptible water has been calculated. Results showed that among the spatial factors, height has greatest impact on the spatial distribution of the maximum of perceptible water. Unlike many scientists who believe that by increasing the latitude perceptible water reduced, this rule is less In Iran atmosphere. However, most of the gradient changes of perceptible water occurred in some parts of the Zagros highlands, West and South West. The results of cycle analysis showed that the maximums of perceptible water in Iran have short term cycles between 2 to 4 years.
Hossein Zarean,
Volume 15, Issue 37 (9-2015)
Abstract
Trees can record long-term effects of climate variables. Using dendroclimatology knowledge, we can reconstruct such variables especially for areas which have short-term climatic data. For this purpose, we reconstructed the temperature degree of the warm months (May-September) through annual rings width of Quercus persica and regression analysis of data obtained from stations on Dena region. With this goal in mind, three growth heights were selected in Dena Forests and 52 growth samples from 26 bases were extracted and their growth rings width were measured with LINTAB5 with an accuracy of 0.01 mm. After cross dating stage, to eliminate non-climate effects, May to September temperature average and tree rings time series were standardized. The Residual Chronology (RES) calculated by ARSTAN was calibrated with temperature degree of the period 1882-2011 and its positive and significant correlation with the width of growth rings was confirmed. Based on the relations between the calculated chronology and joint statistical temperature degree data, the reconstruction of temperature degree of the warm seasons for over a century was performed and it was found that in the last three decades, region's average temperature of May to September had an increase in comparison to the average of the previous century.
Faramarz Khoshakhlagh, Mohammad Amin Heydary,
Volume 15, Issue 37 (9-2015)
Abstract
Climate control centers in each area are diverse and understanding how they relate to the atmospheric components of the Earth's surface contribute to prediction of climate fluctuations. In this study, by using Pearson's correlation and multivariate regression in a thirty-year period (1961-2010), the relationship between widespread rainfall anomalies in entire of Iran west with temperature and pressure of atmospheric centers in East and West of Mediterranean Sea in 5 atmospheric levels (SLP, 850, 500 and 300 Hpa) were analyzed and modeled. Based on the results, the correlation of atmospheric control centers in the East and West Mediterranean Sea with anomalies of rainfall in West of Iran is inverse and meaningful in 95% level. In this study, statistical indicators such as temperature differences and standardized pressure between West and East Mediterranean sea were identified as the most important indicators in relation to changes of rainfall in the study area. Based on the designed indicators, whenever indicators DT and DH is positive, this means higher temperature and higher atmospheric standardized pressure in the Western parts of Mediterranean sea in compare with its East and therefore the wet spells (Monthly) occur in the study area, and If the above mentioned index is negative, means that the occurrence of drought in West Iran. As for the indicators introduced for lower levels of the atmosphere, especially in the case of temperature, meaningful strong and direct correlation is seen with rainfall abnormalities in entire West of Iran. Modeling provided some indicator for Mediterranean region using multivariate regression that they showed a relatively strong correlation in this regard of the selected components that include the pressure difference in sea level, the temperature difference in 925 and 850 hPa level in the West (Compared to its East) Mediterranean sea. Also check the regression model using real data confirm the accuracy of the relative performance of the model.
Maryam Hoseini, Mohammad Karimi, Mohammad Saadimesgari, Mehdi Heydary,
Volume 16, Issue 40 (3-2016)
Abstract
According to urban environment complexity and dynamism and need to targeted land use change, incorporation GIS and PSS in the form of Spatial Planning Support Systems is inevitable. The aim of this study is to develop a spatial planning support system for urban land uses change (ULCMS), such that planners can enter expert knowledge in the form of desired criteria and weights and see their influence in results. The developed system including modules for land suitability evaluation, calculation of the area of required land and land use change. Access models, neighborhood models and Multi Criteria Decision Making methods, fuzzy operators, linear regression, maximum potential and hierarchical optimization models is used in planning and implementation these modules. System practical test performed for measuring residential, commercial, industrial, agriculture and service land use changes for the year 1390 and 1395 in Shiraz city. The result shows that ULCMS help users in better understanding, showing complexity of land use system and development and improvement land management strategies for the creation of better balance between urban expansion and environmental conservation.
Professor Ghasem Azizi, , Leyla Sharifi,
Volume 17, Issue 47 (12-2017)
Abstract
Thunderstorms are major climatic events due to the significant effects and catastrophic consequences on humans and the natural environment. The researches have shown that the elevation and latitude factors are two variables that can affect the occurrence of this phenomenon. Therefore, the main aim of this study is to investigate the spatial analysis of the effects of lightning and its effects on the components such as elevation and geographic extent in Iran. Apart from this fact, firstly, the monthly data of thunderstorms occurrence in 118 synoptic stations of Iran, from 1991 to 2010 on a basis from the country's meteorological organization were obtained and GIS software was produced by the annual and seasonal maps of Iran. Then, for the spatial analysis of this climatic phenomenon, the method of landing statistics of the Kriging (Universal) method was to examine its seasonal and annual status. In order to better understand the effect of Thunder hurricanes from altitude and latitude using Curve Expert software, seasonal and annual charts, along with the correlation of each production, were analyzed. The results show that the highest annual thunderstorms occur in the northwest of Iran, and the least amount is consistent with the central and eastern parts of the country. In addition, according to seasonal analysis, although the station has the highest rate at 800 to 1,300 meters, the maximum occurrence of this phenomenon varies from 0 to 2200 meters in different seasons of the stations. The overall result shows that the factor of height is slightly correlated with the occurrence of the Thunder storm phenomenon and the highest correlation is due to the latitude factor.
Fatemeh Ghiasabadi Farahani, Faramarz Khoshakhlagh, Aliakbar Shamsipour, Ghasem Azizi, Ebrahim Fattahi,
Volume 18, Issue 48 (3-2018)
Abstract
The present research about the spatial changes of precipitation is mainly focused on western areas of Iran. Precipitation data for three seasons of fall, winter, and spring have been obtained from Esafzari Database, with 15*15 km spatial resolution in the form of a Lambert Cone Image System for the period from 1986 to 2015. To examine the prevailing pattern of precipitation in west of Iran, we have used geostatistical methods of spatial autocorrelation. The changes in precipitation trends have been analyzed using parametric and non-parametric analyses of regression and Mann Kendal. We have used MATLAB for analysis of the data. We have also used ArcGIS and Surfer for drawing maps. The results of inter-decade changes of positive spatial autocorrelation of precipitation in west of Iran have indicated that there has been a decline in spatial extent of the positive spatial autocorrelation pattern in spring and fall, except for winter with a negligible increasing trend. Nevertheless, except for the second period, no considerable spatial changes were observed in the spatial pattern of precipitation in the region. However, there was a decreasing trend in the negative spatial autocorrelation of precipitation in annual and seasonal scales. The results of trend analysis have indicated that there was a decreasing trend in a vast area of the west parts of the country in annual scale and also in winter. Although there was an increasing trend in precipitation in fall and spring, but the trend was not significant in 95 % of confidence interval. The results of Man Kendal test have confirmed the results obtained from linear regression.
Dr Abazar Solgi, Dr Heidar Zarei, , ,
Volume 18, Issue 50 (3-2018)
Abstract
Estimating and predicting precipitation and achieving its runoff play an important role to correct management and exploitation of basins, management of dams and reservoirs, minimizing the flood damages and droughts, and water resource management, so they are considered by hydrologists. The appropriate performance of intelligent models leads researchers to use them for predicting hydrological phenomena more and more. Therefore, in this study, the Gene Expression Programming (GEP) and Support Vector Regression (SVR) models were used to model monthly precipitation of Nahavand City. In this study, precipitation, temperature, and relative humidity data were used in a 32-year period (from 1983 to 2014). The results showed that the same and good performance of both models (R2= 0.92), but according to different evaluation criteria, GEP model showed a little better performance (RMSE= 0.0478 and 0.0486), while the running GEP model is so easier than the SVM model. Totally, it can be said that GEP model had been suitable for modeling monthly precipitation of Varayeneh station in Nahavand City. Finally, the monthly precipitation was predicted the GEP which showed a decrease in precipitation in compared with previous months.
Mr Ali Mohammadpourzeidi, Professor Bohloul Alijani, Associate Professor of Climatology Mohammad Saligheh, Mr Mohammadsaleh Gerami,
Volume 19, Issue 52 (3-2019)
Abstract
owledge of spatial rainfall behavior in environmental, land planning is effective. These changes in the later place in the form of time later and in the climate of the area. The Target of this study was to reveal the presence or absence of precipitation trend in the ratio of the height of local precipitation behavior and identify province mazandarn. Therefore, the purpose of the rainfall data station 32 (Meteorological Agency and Department of energy), the statistical period 1988-2010. To get the regression analysis of precipitation process was used to identify the local behavior of precipitation, the method of spatial statistics were used. The results obtained from the behavior of precipitation, the existence of the process within the scope of the study and the emphasis is most consistent with the Be modified regression model at adjustment indicate. According to the regional behavior of precipitation, using local spatial statistics, spatial Moran well hot spots check this behavior. The results showed that precipitation in the province of Mazandaran has the pattern of clusters with high value. According to the local hot spots and methods Moran, West Coast up to a height of 700 m has positive z score and clusters with high value, 99% confidence level. This range includes 15% of the total of the province. The range of the Southern Highlands as well as the negative z score and clusters with low value with a confidence level shows 99%. This range is also about 20 per cent of the province's total. About 65 percent of the total area of the province as well as the lack of a significant trend show.
Dr Yagob Dinpashoh, Miss Masoumeh Foroughi,
Volume 20, Issue 58 (9-2020)
Abstract
Reference evapotranspiration (ET0) is a climatic parameter and can be computed from weather data. It is one of the most important hydrological parameters for calculating crop water demand, scheduling irrigation systems, preparing input data to hydrological water-balance models, regional water resources assessment, and planning and management for a region and/or a basin. The climatic data from synoptic stations with more than 20 years continues record in West Azarbaijan province was used. The well-known FAO-PM56 method was used to calculate the ET0. Then Multiple linear Regression (MLR) was used to estimate the ET0. The RMSE, MEA, NSH, and R2 were used to evaluate the performance of the MLR model. Then, the correlation coefficient (r) between ET0 and each of the meteorological parameters was obtained. And finally, with using Path analysis method, the influence of direct (P) and indirect effects of the meteorological parameters on ET0 was calculated. In the studied synoptic stations, NSH between 0.91 and 0.99, R2 between 0.91 and 0.99, RMSE between 0.05 and 0.15, and MEA between 0.04 and 0.12 were obtained. Also, it was found that the wind speed at all of stations had a significant correlation (at the 0.01% level) with ET0. The path analysis results showed that the maximum value of P (direct effect of meteorological parameters on ET0) in all of the stations belongs to wind speed. The P value of wind speed in Urmia equal to 0.85, Piranshahr equal to 0.99, Takab equal to 0.97, Khoy equal to 0.90, Sardasht equal to 1.06, and Mahabad equal to 0.78 are obtained.
Ali Shamai, Mahsa Delfannasab, Mohammad Porakrami,
Volume 20, Issue 59 (12-2020)
Abstract
The purpose of this study was to investigate the factors affecting housing prices in the Laleh Park district of Tehran. In this study, the data of all real estate traded in the first six months of the year 2016 was used in the study area. Information about the physical properties of the residential units trained is collected from the Real Estate Market Information System of Iran and is used to obtain information on the accessibility features of residential units traded using ARC GIS software. Multivariate regression analysis has also been used to investigate the factors affecting housing prices. The results of this study showed that the physical factors of housing are more effective than the access factors in the housing prices in this district . Among the selected features, the variables of residential area, parking, and skeletal type had the most positive effect on the price of housing in the area under study. On the other hand, some of the features, such as the distance from the residential unit to the nearest main street, the residential unit to the nearest educational user, the residential unit distance to the nearest health care provider, and the residential unit's age, had a negative effect on the housing price in the Laleh Park district .
Farahnaz Khademfesgandid, Dr , Dr Maryam Singery, Dr Mahsa Faramarzi Asl, Dr Samad Sabag Dehkhargani,
Volume 21, Issue 60 (3-2021)
Abstract
The degree of success of urban spaces is commensurate with the extent to which it can be utilized and the communication and communication that it can provide. What we are facing today in most urban spaces is the decline of human-environmental and human-environmental relationships. This study seeks to evaluate the extent of social interactions in these two paths and attempts to provide an optimal solution in this regard. Historical, appropriate physical structures And ... have been studied and divided into two sub-components of physical components such as existing values and attitudes regarding physical components, and regarding subjective sub-components of mental imagery, user interests In this study, we tested the t-components and sub-components mentioned above. The research hypothesis is the effect of physical and non-physical elements and components on the formation of interactive spaces for communication. Man was endorsed by the environment.
Alireza Rahimi, Nader Nazemi, Jamaleddin Honarvar,
Volume 21, Issue 60 (3-2021)
Abstract
Energy plays a major role in providing welfare of urban and rural households, and reforming energy consumption patterns, in addition to price balancing, requires recognition and acts of cultural and social variables affecting the pattern of consumption and savings. Considering the importance of saving electricity and its relation with consumer behavior, in this study, the difference in urban and rural communities was investigated in terms of effective factors on energy savings. The present research is descriptive-analytical in terms of purpose and method. The data-gathering tool and information collection and interviews with urban and rural households in Poledokhtar city. The statistical population includes urban and rural households in Poledokhtar Township (N= 30012). Using Cochran formula and simple random sampling method, 379 households (244 urban households and 135 rural households) were selected. In the data analysis section, analysis of variance and logistic regression tests were used. The results showed that there is a significant difference between the factors and indicators affecting power saving in rural and urban areas. The individual agent and the factor of behavior management and purchasing, while the factor is the most important factor in saving households in rural areas, primarily influence power saving in urban areas.
Msc Taraneh Mirgheidari, Dr Behzad Rayegani, Dr Javad Bodagh-Jamali,
Volume 22, Issue 65 (6-2022)
Abstract
This study was conducted with the aim of providing a remotely sensed water quality index in Assaluyeh port using remote sensing technology. so, according to the region conditions, studying of scientific resources and access to satellite data, the parameters of heavymetals, dissolved ions, SST, chlorophyll-a and pH were selected. Then, by reviewing sources, the product MYD091km, MYD021km, MOD021km, MOD091km and level2 images of chlorophyll-a and SST of MODIS sensor were used after preprocessing operations. Also In-situ data were collected Simultaneously with the capture of satellite images in August 2014. Then, the relationships between the water quality parameters and MODIS data, with (R2) from 0.59 to 0.94 and (RMSE) from 0.07 to 0.1 were obtained. Next the images of the MODIS sensor from 2015 to 2017 were prepared and the models were applied to them, then the layers were standardized by fuzzy logic. Also time series of SST data from 2003 to 2017 were prepared and for each month the average pixel values were calculated and based on this, from 2015 to 2017, the variation of this parameter was standardized. Finally, an effective index for assessing the quality of coastal waters was provided by time series of satellite images and the waters of Assaluyeh port were zoned. The results showed that the water quality in 2015 and 2016 has shifted from poor to very poor status in 2017. Based on the results, with the development of a proposed index, in future studies a continuous assessment of environmental monitoring is possible.
Dear Dariush Abolfathi, Dr Aghil Madadi, Dr Sayyad Asghari,
Volume 22, Issue 66 (9-2022)
Abstract
The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLP's neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.
Maryam Aghaie, Siamak Dokhani, Ebrahim Omidvar,
Volume 24, Issue 74 (9-2024)
Abstract
Rain water harvesting is an appropriate option for storing surface runoff for subsequent uses during periods with limited access to water. The most important step in the application of rainwater harvesting systems (RWH) is the site selection suitable areas. Therefore, by identifying suitable sites for this purpose, time and cost will be saved . In this research, multivariate regression model and GIS were used to site selection in situ (RWH) in Tajare watershed. For this purpose, layers such as crown cover, litter, rock and stones, soil, curve number, rainfall, slope and depth of field as independent variable and infiltration were considered as the dependent variable. Then, according to the maps, their values were calculated in average for each of the 27 sub-basins. Also, to investigate the relationship between these variables and weighting, each of the effective layers of multi-variable regression was used by the stepwise method The results showed that the linear multivariate regression model with an explanation coefficient of 0.993 was able to estimate the penetration factor values well In terms of grade of importance, the curve number variables with a coefficient of -2.433, depth of soil with a coefficient of 0.3488, and rubble and gravel percent with a coefficient of 0.057, were the most important, and other factors were not significant. Comparison of the map from the site selection of multivariate regression in this research with some recommended criteria of various research studies showed that the predicted classes with good in the central parts of the basin and very good in the upstream areas of the basin which in the eastern and southeastern part of the basin fit have a good overlap with the recommended areas with these criteria.
Mr Shokrollah Kiani, Mr Ahmad Mazidi, Mr Seyed Zein Al-Abedin Hosseini,
Volume 24, Issue 74 (9-2024)
Abstract
Subsidence is an environmental phenomenon caused by the gradual subsidence or sudden subsidence of the earthchr('39')s surface. The phenomenon of subsidence in residential, industrial and agricultural areas can cause catastrophic damage. In most parts of Iran, there is a high correlation between land subsidence and the decrease of groundwater level and consequently the density of soil layers. In this study, using two time series of radar images with artificial apertures from Sentinel sensors belonging to 2014 and 2019, the amount of subsidence in Damaneh plain (Frieden city) was calculated. Wells were studied in the period 2014 to 2019, the results of the study of the correlation between land subsidence with changes in groundwater level at the level of 95% was significant. In the continuation of the research, using the logistic regression model, the subsidence trend in the study area was predicted and a subsidence probability map was prepared and created as a dependent variable for the logistic regression model. The independent variables used included altitude, slope, slope direction, geology, distance from the road, distance from the river, land use, distance from the village, groundwater level, piezometric wells. The output of the model is subsidence risk zoning map which was created in five classes. The accuracy and validation of the logistic regression model was evaluated using the system performance characteristic curve and the accuracy (0.89) was obtained. The good accuracy of the logistic regression model in producing the probability map Subsidence is in the study area. In the output of the model, it was found that the area of 1980 hectares, equivalent to 7.9%, has a very severe subsidence that has put the situation in a dangerous situation and the need for control and management to reduce this destructive effect.
Ali Hashemi, Hojjatollah Yazdanpanah, Mehdi Momeni,
Volume 24, Issue 75 (12-2024)
Abstract
This research study aims to investigate the effect of climatic variables, specifically precipitation, temperature, and humidity, on changes in vegetation indices of orange orchards in Hassan Abad, Darab County, using satellite data. Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on territorial data and 1:25000 maps from the Iran National Cartographic Center. These images were used to calculate remote sensing vegetation indices, namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on both the NDVI and EVI. To determine the significance of each independent variable in predicting the dependent variables, the artificial neural network method was employed. The findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity, and maximum humidity had the greatest effect on EVI, with values of 0.39, 0.3, 0.13, 0.1, and 0.06 respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients, which are 0.2, 0.28, 0.22, 0.11, and 0.17 respectively. Finally, the ARMAX regression method was used to improve the explanatory power of the model. The results indicated that this method enhanced the explanatory power of the model and reduced the forecasting error.
Seyed Komeil Salehi, Ms Habibeh Nabizadeh, D.r Amineh Anjem Shoa,
Volume 25, Issue 77 (6-2025)
Abstract
The purpose of this study was to investigate the factors affecting the increase in attractiveness of tourism purposes in Tehran. The present research is descriptive-analytical in terms of purpose and method. The data collection tool is a question and interview. The statistical population of the study includes experts and experts in the field of tourism, which was selected using Cochran formula and simple random sampling method, 210 tourism experts were selected as samples. Descriptive tests and logistic regression test were used to analyze the data. The results of this study indicate that from 210 active in Tourism in Tehran, 91 people believed in 43.3%, with attractiveness of tourist destinations in Tehran at high level, 29% believed that the level of charm at the appropriate level and only 27% He believed that the attractiveness of tourist destinations in Tehran is at a low level. The results in the field of effective factors on increasing the attractiveness of intentions due to tourism development also showed that among the four factors intended, respectively, factors of 1) innovative business opportunities with impact coefficients (613/0), 2) assets Natural / cultural and historical city with a coefficient of impact (0.577), 3) Development of tourism infrastructure with an impact coefficient (0.497) and 4) urban development agent with an impact coefficient (0.473) had the most effects on increasing attractiveness Due to tourism development in Tehran.
