linear regression

Land surface temperature, as one of the important and fundamental parameters in climatology, indicates the relationship between the atmosphere and the Earth. Considering the environmental issues of cities, including the intensification of urban heat islands, accurately estimating LST and identifying its influencing factors play a significant role in urban thermal management and adopting adaptive strategies for heat islands. In this regard, this study compares two regression methods multiple linear regression and random forest in order to estimate the LST. Daily nighttime MODIS images were used to extract the LST of Tabriz city during the summer. These images were processed in the Google Earth Engine platform and averaged for the period from 2018 to 2022. According to the results, the random forest showed significantly better performance with a coefficient of determination of 0.924 (RMS = 0.009) compared to multiple linear regression. The random forest was also used to determine the importance of the indices. Based on the index importance results, night lights (51/06%), sky view factor (48/01%) and frontal area index (45/27%) were the most important factor affecting the nighttime summer LST in Tabriz city, respectively. The findings of this study, in addition to revealing the strength of the random forest regression in estimating LST, also highlight the importance of various indices in the LST. In this context, the study's results will be practical for managing the thermal environment of Tabriz city and adopting mitigation strategies for its heat islands.

Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R = -0.85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0.58, indicating good accuracy of the model.

Currently, many scientific evidences indicate a strong correlation between the earth's surface and its functional features and coverage in cells. Various patterns of settlement development have a direct impact on the formation of urban heat islands, and the use of spatial models plays a crucial role in enhancing the understanding of these effects. Therefore, in the present study, a cellular automata model was employed to predict the future development of residential areas in northern Iran (Guilan plain) for the years 2035 and 2050 under three urban growth scenarios: Business as Usual (BAU), Environmental Protection (ENV), and Compact Growth (COM).

In this research, a combined modeling approach was utilized to extract historical patterns of residential expansion and land surface temperature for the years 2002, 2012, and 2022, using cloud-free Landsat images. Subsequently, the cellular automata model was employed to predict the future development of residential areas for the years 2035 and 2050 under three different scenarios. The relationship between land surface temperature and residential areas was examined to identify the most effective regression models explaining the impact of residential expansion on the average land surface temperature of the region.Urban growth modeling was executed using the Cellular Automata-Markov model. For this purpose, settlement layers from 2002 and 2012 were employed to train the Markov model, and the transformed land-use ratio was estimated for constructed areas until 2022. Additionally, layers from 2002 and 2022 were utilized to determine the area and probability of land transformation until 2035 and 2050. A multi-criteria evaluation method was applied to prioritize land transformations and formulate various urban growth scenarios. The factors for the multi-criteria evaluation included soil quality, erosion, fault lines, proximity to roads, provincial centers, various sizes of urban areas, as well as forest and agricultural categories. Constraints, scenario-dependent, considered river basins (and their 200-meter boundaries), water bodies, impermeable surfaces, and forested areas. Ultimately, a weighted linear combination method was employed to integrate layers of factors and constraints, creating suitable layers for each scenario, including the Business as Usual (BAU) economic growth scenario with the aim of simulating growth, Environmental Protection (ENV) scenario focusing on environmental conservation, and Compact Growth (COM) scenario aiming to prevent the fragmentation of agricultural lands. For the integration of layers, a hierarchical analysis process was utilized, and for the transformation of values for each factor and constraint layer into dimensionless comparative scales, fuzzy membership functions and Boolean logic were applied. For modeling land surface temperature, two spatial feature-centric approaches were employed, namely the patch-based and segment-based perspectives.

The land surface temperature layers of residential areas were retrieved from three Landsat satellite images for the years 2002, 2012, and 2022, with average values of 33.14, 36.38, and 34.78 degrees Celsius, respectively. Statistical analysis results indicated that spectral segments extracted from object-based image analysis provide more accurate predictions of the average land surface temperature. Using the area of spectral segments and their common border percentage with neighboring segments, a regression model was constructed to predict the land surface temperature of residential areas for the years 2035 and 2050 (R2 = 0.617). The highest average land surface temperature was obtained under the COM scenario (33.88 degrees Celsius) in 2050, while the lowest was observed under the ENV scenario in 2050 (31.23 degrees Celsius).The results of this study indicate that this correlation will be meaningful at smaller structural scales. In other words, urban blocks act as distinct residential units, each having its specific land surface temperature. Based on these findings, it seems that studying land surface temperature at the block scale, especially in larger and interconnected residential areas, offers greater capability in estimating land surface temperature. Despite this approach, structural criteria of segments could not establish a significant correlation with their average land surface temperature.

The results illustrate that the land surface temperature of residential areas may not accurately reflect the historical trend of temperature changes due to local land conditions. Therefore, models based on the relationships between temperature and land surface features were used for modeling in three different time periods. The extent of residential segments demonstrated a strong statistical association with the average land surface temperature, while structural features, such as neighborhood parameters, could not predict changes in residential land surface temperature. Based on the findings of this study, the average land surface temperature of residential areas is dependent on both the spatial configuration of residential spectral segments at the local scale and the patterns of residential area expansion (scenarios) at the regional level.

The city of Tabriz, which has the fastest urban growth in the northwest of the country, is one of the largest cities in Iran in terms of population, economic activity, industry and transportation options. Public transportation and industry combustion and lack of proper filtration of these industries, such as thermal power plants, has led to increased air pollution in the city. For this purpose, the present study tries to use input variables (distance from industrial centers, humidity, temperature, population density, distance from commercial centers, distance from bus stations, distance from educational centers, vegetation changes, distance from free Roads, building density, wind direction, carbon dioxide and carbon monoxide) to assess air pollution using artificial neural networks in the metropolis of Tabriz. In the present study, the independent variables affecting the distribution of pollution probability in two models of multilayer perceptron neural network (MLP) and linear regression were tried to be defined by defining measures in urban management and influencing and planning the mentioned variables. Improve pollution control.The results show that the major pollutants are mostly suspended particles (PM10), gas (CO2), (SO2) and (NOx).The dispersion of airborne particles is mostly due to vehicle traffic, industrial activities, fuel combustion of diesel engines and construction and the need to generate more electricity.-The activities of thermal power plants, Tabriz refinery and domestic and commercial heating systems are also among the factors producing SO2 and the highest CO2 production is related to the fuel of gasoline-burning vehicles. The intensity of the increase in the amount of this pollutant in all selected stations in the autumn and winter seasons is much higher, so that in these seasons the pollutants reach more than twice the allowable level.The share of Tabriz air pollutants can be divided into three general categories, the most important of which is the thermal power plant and transportation.

nvestigating the relationship between urban green space on air pollution and respiratory mortality in Mashhad using linear regression was done in 2018.

The study method in the present research is descriptive-analytical, which investigated the relationship between variables using GIS and TerrSet software. Therefore, in the first step, after receiving air pollution concentration data from 23 air quality control monitoring stations in the city of Mashhad and applying the necessary corrections to it with the interpolation method in GIS software, the distribution and distribution of pollutants was prepared in the form of a map. In the second step, the statistics related to Mortality due to respiratory death was received from Ferdous organization of Mashhad city and the distribution and density map of respiratory deaths was drawn. Finally, the obtained maps were called in TerrSet software and the relationship between variables was measured using the linear regression option.

The results showed that the concentration of nitrogen dioxide in the north and northeast, carbon monoxide in the north and central areas, and the concentration of respiratory deaths in the northeast were higher than in other places. The relationship between air pollutants and respiratory mortality with urban green space using linear regression also indicated that the variable of green space with nitrogen dioxide has a correlation coefficient of 0.11, carbon monoxide has a coefficient of 0.03 and respiratory deaths has a coefficient of 0.046.

The results showed that despite the low correlation coefficients, the increase in urban green space has led to a decrease in air pollution and the number of respiratory deaths in Mashhad.

Innovation: 

Among the most important innovative and practical aspects of the research, we can mention the spatial distribution of respiratory deaths in Mashhad in 2017 and its relationship with urban green spaces.

Changes in hydrological-climatic series can occur in different ways. Change can occur suddenly or gradually (trend) or in more complex forms. In general, trend analysis is performed to obtain information and study whether a trend or a pattern can be extracted from this information. Information about precipitation trends is important, because precipitation trends are related to water-related problems in the region, environmental and water management goals. This information will be the most valuable when studying climate change and its effects on water resources management. Determining the precipitation trend has been one of the most important activities of hydrologists and meteorologists in advancing climate change studies. In addition, the study of climate change requires information on the trend of various indicators (hydrological and climatic) because climate change is a continuous change. Almost all water and meteorological parameters are affected by climate change phenomenon. Precipitation is one of these variables that strongly affects the environment and the hydrological cycle. The temporal changes of precipitation are important both from scientific and practical point of views, since they strongly affect water resources. Therefore, studying the process of these changes is very important for short-term and long-term management planning. The importance of this study increases when we face time and place limitations of rainfall in an arid and semi-arid region like Iran and especially in Fars province. Therefore, in the present study, the 30-year trend of precipitation was investigated in 27 rain, synoptic and evapotranspiration stations in Fars province. The study of rainfall changes was done at annual and seasonal and at both stational and regional scales. At the first step, monthly precipitation data was gathered from meteorological organization and water resources management company. Then monthly data was converted to seasonal and annual data sets. Mann-Kendall methods, age slope estimator and linear regression analysis were used to conduct at point analysis. In order to study the rainfall trends at regional scale, first, the study area was clustered based on mean seasonal and annual precipitation using the K-mean clustering method. It is worth mentioning that for each season and annual, separate homogenous regions were formed. Then regional Mann-Kendall method was utilized to investigate the trends in each homogenous region. On an annual scale, a decreasing trend of precipitation was observed at all stations. But Arsanjan, Berghan, Tangab Firozabad and Farashband stations are the only points whose rainfall trends are significant at the 95% confidence level. On a seasonal scale, a decreasing trend of precipitation was observed in almost most seasons and most of the stations. Except for the summer season, when we see a very slight increase in the majority of the stations. In spring and summer, respectively, 48% and 67% of the stations witness a decrease in precipitation, but these trends are not significant. In the autumn season, 74% of the stations experienced a negative trend of precipitation, and among these stations, Arsanjan, Berghan, Shiraz, Farashband, Kazeroon, Madersaliman and Zarghan witnessed a significant decreasing trend at the 95% confidence level. At the regional level, on annual and seasonal scales, the entire province has seen a decrease in rainfall, especially in winter and annual rainfall. This decrease in rainfall in the northwest of the province is a little weaker than other parts of the province. In the spring season, decreasing changes were observed, but these changes are not significant. In the summer season, a significant increase was seen in rainfall in the southern part of the province. This may be due to the increased activity of the Indian Monsoon. In the autumn season, a decreasing trend in the whole province was experienced, which is not significant. Similarly, the winter season also a decrease in precipitation was observed, with the difference that this trend is significant at the 95% confidence level. In general, all point and regional analyzes show a decrease in precipitation, especially in the annual and winter scales. On the other hand, there are signs of an increase in rainfall in the summer season. Considering that most of the province's annual rainfall occurs in the winter season, the decrease in rainfall in this season can have irreparable negative effects, which of course it has done so far. Therefore, it is expected that more attention must be paid to the planning of the water management and agriculture. In addition, due to the signs of increased rainfall in the spring and summer seasons, there should be sufficient attention to the problem of flooding in these seasons and the resulted damages, and in general, the comprehensive management of water resources in these changing conditions should be seriously considered so that the minimum losses and maximum benefits could be obtained.

Temperature is one of the main factors influencing urban planning because it guides the type of facilities available in cities and even determines the urban structure, shape and texture. On the other hand, increasing vegetation cover is one of the most effective strategies to reduce urban climate impacts. In the present study, the land energy balance algorithm was used to achieve land surface temperature and the soil vegetation index of Nazarabad city was used to estimate plant poisoning. For this purpose, Landsat satellite images (TM-OLI), years (2000-2019) were used. First, the relevant images were obtained and the necessary preparations were made. Then, the object-oriented classification was performed. Linear regression analysis was used to estimate the correlation between surface temperature and vegetation. Surface temperature results showed that vegetation areas such as agriculture and rangelands averaged 32 and 34 ° C for 2000 and 2019, respectively. However, during these years, vegetation-free areas such as urban and wilderness areas that were devoid of vegetation have averaged 36 and 39 degrees Celsius, indicating the effect of vegetation on surface temperatures. Also in estimating the correlation between vegetation and land surface temperature, it can be said that there is a strong correlation between the data. Independent variables have been explained which show high value and the model is significant with 0.95% confidence and is able to express changes based on available data so it can be concluded that this model has high accuracy for this study.

Severe coastal winds, either directly or by flooding the coastal waters, can damage installations or cause disorder in various human activities such as shipping, commerce, recreation, etc. This study investigated the temporal-spatial variability of maximum coastal winds using the maximum wind data from stations in Iran's northern and southern coasts. Statistical analysis of the average maximum wind speed showed that the Persian Gulf coast had the highest speed. The monthly changes of the maximum wind indicated that the maximum monthly average is related to November in Anzali port and the minimum rate is for Chābahār station in all months. Generally, the variability of summer and autumn months is less than spring and winter. The maximum wind direction on the shores of the Persian Gulf and the Caspian Sea has a clockwise shift from east to west. Linear regression analysis was used to study the trend of maximum wind speed, which indicated the heterogeneity of the line slope of the stations on the southern coasts and the homogeneity of the northern coast stations with a positive slope.Highlight The maximum average monthly is related to November in Bandar-e-Anzali and the minimum speed is related to Chabahar station in all months and the variability of summer and autumn is less than spring and winter seasons.The topography altitude in the coastal areas and their distance to the coast, play a decisive role in the direction of maximum winds.Linear regression analysis shows the maximum wind time series have a negative slope on the south coasts (except Chabahar and Abadan stations) and a positive slope on the north coasts.Extended Abstract Introduction Coastal areas are the confluence of hydrosphere, atmosphere and litosphere, and this triple link provides special biological conditions for humans and other living organisms. Due to the special characteristics of coastal areas, human life and activity in these areas are affected by the intense functions of natural factors. One of the most important natural factors in these areas is wind, and as the intensity of these winds increases, the effects increase. Severe coastal winds, either directly or by flooding the coastal waters, can damage installations and disorder in various human activities such as shipping, commerce, recreation, etc.In this study, the monthly data of speed and direction of maximum wind of the Meteorological Organization for coastal stations on the coasts of Oman, Persian Gulf and Caspian Sea were used in the thirty-year period of October 1992 to October 2021.MethodologyFirst, descriptive statistics related to each station were calculated. The maximum wind vane of each station was plotted. The frequency of wind vector events for each station was calculated and plotted. Linear regression related to each station was calculated to determine the temporal variations of maximum wind in each station.Results and discussion The study of the average maximum wind speed shows that the Caspian Sea coasts with an average of 12.75 meters per second have the highest and the Oman Sea coasts with 10.97 meters per second have the lowest average maximum winds; this amount in the Persian Gulf coasts is about 12.3 meters per second. Investigation of monthly changes of maximum wind shows that the maximum average monthly is related to November in Bandar-e-Anzali and the minimum speed is related to Chabahar station in all months and the variability of summer and autumn is less than spring and winter seasons. The severe winds direction in Chabahar station are from the west-southeast and in Jask station are from the west-eastern. The direction of the maximum wind on the shores of the Persian Gulf changes clockwise from east to west stations, so that from the south in Bandar Abbas, finally it turns north in Abadan; in the shores of the Caspian Sea, from east to west (Gorgan to Astara), the prevailing wind, like the shores of the Persian Gulf, moves clockwise and changes from west to north. On the shores of the Caspian Sea from east to west (Gorgan to Astara) the prevailing wind, like the shores of the Persian Gulf, has a clockwise movement and changes west to north (except Astara). In general, due to the maximum winds are the result of pressure daily changes and considering the clockwise changes in wind direction from east to west on both the north and south coasts of Iran, it can be concluded that the topography altitude in the coastal areas and their distance to the coast, play a decisive role in the direction of maximum winds. As a result, in areas where the altitudes are low or far from the coast, high pressures are formed in the water zones and therefore the direction of maximum winds are from the sea to the land. Linear regression analysis shows the maximum wind time series have a negative slope on the south coasts (except Chabahar and Abadan stations) and a positive slope on the north coasts. The highest line slope is related to Gorgan station in the easternmost part of the Caspian Sea coast.ConclusionOverall it can be said that with more concentration of human activities on the coast and an increase extreme climate events, including severe winds, the probability of human and financial losses on the coast has increased and the need for identification and planning to prepare with such hazards is inevitable. According to the results of this study, which shows the increasing trend of severe winds in the northern regions, and also due to the intense concentration of population on the northern coasts, this hazard must be taken seriously. The use of coastal meteorological stations to predict and warn of severe winds, the correct location of piers, strengthening of coastal structures and construction of coastal walls to deal with strong waves caused by severe winds and Educate local residents to deal with severe winds can reduce the risk of severe damage during these events.FundingThere is no funding support.Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the workConflict of Interest Authors declared no conflict of interest.AcknowledgmentsWe are grateful to Iran meteorological organization for providing data in this paper.

Volcanic activity can cause climate fluctuations by disrupting the entry of sunlight. In this study, the effect of major volcanic activities in the world on temperature changes in Iran has been investigated. To achieve this, first 16 meteorological stations with long-term statistics in Iran were selected and a long-term temperature series was received from the Meteorological Organization. Then, VEI and DVI indexes were used to select major volcanoes and six major volcanoes were selected. SEA and multiple linear regression techniques were used to investigate the effect of major volcanic activity on temperature in Iran. The results showed that after major volcanic eruptions, the effects of decreasing temperature can be observed with time delays of zero, one and two years, and the greatest intensity of temperature decrease was observed in the first year after the occurrence of the volcano. Comparison of the three variables of mean temperature, minimum mean temperature and maximum mean temperature showed that volcanic activity has the greatest decreasing effect on the mean maximum temperature. The results of the study to separate the seasons also showed that in winter the intensity of the decreasing effect occurs more, but in summer the decreasing effect of volcanoes lasts longer. Finally, the findings of this study show that on average, in the year of the occurrence of major volcanoes, a decrease of -67.0 degrees Celsius, in the first year after the eruption, the rate of decrease of -1 degrees Celsius and in a two-year delay, the average decrease. Temperature -0.47 degrees Celsius has been observed. Therefore, it can be confirmed that the eruption of major volcanoes has affected the temperature of Iran and has caused a decrease in temperature in Iran.

The issue of climate change and the tendency of global warming and its consequences, in addition to scholars, has attracted the thoughts of government men and politicians around the world. Climate is one of the most important components of the ecosystem and parameters Climate Temperature and precipitation are sensitive to climate change. Therefore, examining the trend in such variables can help in identifying climate change in the region. In order to study and detect climate change, the study of a number of climatic parameters has a higher priority than other parameters. The aim of this study was to investigate the trend of annual temperature changes in Urmia synoptic station by Mankendal test in a statistical period of 15 years with a time interval (1996-2010 in Sinotec meteorological station of Urmia. Statistical method of linear regression and Fan Kendall statistic for hair Significance of trend of temperature and precipitation series changes was used.The results show that the lack of temperature trend in Urmia has decreased with a downward slope and the lack of synchronous rainfall trend of Urmia with rising valve is increasing.

In recent decades, dust storms have intensified due to climate changes, including drought and human interventions. In addition to removing soil particles, dust brings storms bring about severe economic, social, and health problems. Due to its strategic conditions – including its status as an industrial and agricultural hub and the province hosting more than 29 million Iranian and international pilgrims, especially during the seasons and months with specific holidays – should be prioritized in the examination of dust storm frequency. The purpose of this study was to examine and track the frequency of dust storms and analyze the effect of climactic changes on them through the investigation of drought trend as well as the trend of changes in the land surface coverage percentage during the statistical analysis period. The results of counting the dust codes showed that dust storms with local origin had the highest frequency. According to the monthly analysis, Sarakhs and Mashhad stations had the most dust phenomena, with the most dusty days in the study area during summer and spring seasons, especially June and July months. The results of regression analysis of drought index and the monthly local dusty days indicated the existence of a significant, positive relationship between dust increase and drought intensity in Mashhad and Sarakhs stations during the statistical period of 27 years at 99 percent confidence interval. Finally, in the light of the results obtained from the examination of the land surface changes, it is recommended that land use plans be used along with short-term methods of soil stabilization in order to reduce the economic damages and preserve people’s health against dust during busy months and seasons so as to achieve the goals of long-term methods.

The present study seeks to measure the variables that affect the formation of reverse migration in the villages of central part of Dezful city. This research is a practical and descriptive- analytical methodology. Documentation and fieldwork have been used to collect information. Sampling method In this research, quotas were sampled and questionnaires were filled out by simple random sampling between households. The results of t-test showed that, from the viewpoint of immigrants, economic index is the most important factor affecting the formation of return migration. Also, the results of linear regression test indicate that the variables of age, sex, native belonging and residence distance are influential in return migration. According to Friedman's test, access to underlying, communication, welfare and administrative services has been more important than other variables. Finally, the results of the fuzzy system indicate that the studied villages are in total the return of high migration.

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 MLPchr('39')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.

The rail transport system consists of the interaction of a set of equipment and operations that determine the capability and capacity of a rail system in freight and passenger transport. For this purpose, it is important to calculate the capacity and predict how it will change, and knowing it will be of great help in improving the level of operation of the railway network. There are several methods for calculating capacity that can be used depending on the type of network and how it is used. To calculate the capacity, the capabilities of spatial information systems are used and with the help of a web-based spatial information system, the operational capacity of the rail network is determined in a new way and with more efficiency than conventional methods. For this purpose, a GIS-based environment that is connected to various databases of the Railway Company of the Islamic Republic of Iran, including the travel database, is used and while observing the current capacity of the network, through multivariate linear regression, the capacity of the rail network in It determines the future. The present study, through linear regression, predicts railway capacity in a case study in Iran for the three selected routes and identifies important blocks for investigating the effect of spatial parameters in determining the capacity of the railway network. Slowly Based on the available data of 1996 (extracted from the Railway Spatial Web Service), capacity forecasting was performed in 1997 in the GIS environment. The results showed that the capacity utilization of the selected routes for freight trains was 82%, passenger routes 56%, 62% return and 79% combined routes. Also, the accuracy of model prediction for freight trains is 35% better than passenger trains, which is due to the difference in speed change and maximum speed allowed for these two types of trains, and modeling accuracy is directly related to the type of part. Route (passenger, freight and combined), so in the passenger route, the modeling capacity of passenger trains was approximately 45% more accurate. Similarly, on the freight route, the estimation of the capacity of freight trains was associated with approximately 45% higher accuracy than that of passenger trains.

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.

The house is one of the main requirements of human to achieve comfort and relaxation. It plays an essential role in life’s quality and is an index of welfare in a society. According to the central bank of Iran reports, the housing share in households has increased by more than 30 percent from 2008 to 2017. This rate increased by 43 percent in 2017. These values are also higher for lower deciles. In 2016, 78% of low-income households’ income was spent on housing and food. Therefore, these families unable to enjoy urban amenities. Also, due to the annual movements which cause many problems of hiring and stresses, the priority of citizens is house purchasing. Therefore, families search in the city to find suitable apartments according to their needs and budget. Awareness of property prices is important for both homeowners and customers. If buyers know the prices of apartments in the area, not only find the most suitable unit based on their needs and budget but may not also be defrauded by some individuals. Researches show that residents of each area look for apartments around the previous areas for purchase or rental of residential units. The price information gives people more freedom and confidence to find the best cases, and save time and money instead of limit themselves to the surrounding area.

The soil salinity is one of the most common and important factors in soil degradation in arid and semi-arid regions. It is important concern to predict and monitor soil salinity. In recent years, using remote sensing techniques to monitor soil salinity has been significantly developed and various models have been extended for this purpose. Among all the methods, linear regression is the most commonly used due to its simplicity and computational efficiency. In Iran, many lands are faced with a significant increase in salinity. Beaches of Urmia Lake is one of these area. The purpose of the present study was to evaluate the capability of Sentinel multispectral imagery as well as to compare univariate and multivariate linear regression models to estimate soil salinity at 0 to 10 cm depth in the eastern margins of dried lake-bed of Urmia. For this purpose, 38 soil samples (training and test samples) were collected from three different locations A1, A2 and A3 in in this study area with different salinity values at the satellite transit time. Then their electrical conductivity (EC) was measured in the laboratory. The samples were collected near accessible roads by three mapping teams. Of the total number of samples, 28 and 10 sample were considered as training and test samples respectively. Then the Sentinel 2B multispectral satellite image was prepared by resampling simultaneously on October 6, 201 .In this research, eight spectral bands of the Sentinel image (visible and infrared bands) and 17 salinity indices were utilized. Then, in each of the spectral bands and salinity indices, different univariate linear regression models were calibrated to estimate soil salinity. As well as multivariate linear regression models were designed using simultaneous spectral bands and soil salinity indices. The accuracy assessment of both methods was estimated using 10 test samples by the coefficients of determination and Root Mean Square Error parameters. In univariate linear regression model, the best (results) model for estimating soil salinity were presented of narrow infrared band (8a) and BI salinity index with the highest and lowest values of and RMSE based on test samples, respectively. The and RMSE were obtained for band 8a and BI index 0.89, 0.83, 20.85 and 21.33, respectively. Compared to univariate linear regression models, the proposed method in this paper is based on multivariate linear regressions with 7 variables provided the highest accuracy among all multivariate and univariate regression models. The and RMSE based on test samples were obtained for multivariate linear regressions with 7 variables 0.97 and 8.77 respectively. Finally, soil salinity maps of the area were prepared with the best regression models. Model evaluation results showed that multivariate linear regression models increased the accuracy of soil salinity estimation by 11.81% in comparison with univariate regression models. These results showed the potential of multivariate linear regression model and multi spectral sentinel image to estimate soil salinity content in dried lake-bed of Urmia Lake.

Monitoring the changes in land surface temperature (LST) caused by seasonal and non-seasonal fluctuations is essential due to its profound impact on the human and natural environment. The use of remote sensing satellite imagery has been extensively considered for the continuous monitoring of LST with its low cost and high speed. In this research, first, three conventional temperature extraction approaches based on Plank’s law were used to extract LST of surrounded areas of Lake Urmia. Then, using a linear regression and observations of the temperatutre in meteorological stations, LST maps extracted from conventional methods were modified. The results showed that conventional temperature extraction approaches for TM and TIRS sensors of Landsat Satellite had an accuracy of about 4 °C and 8 °C, respectively. After modifying LST maps using the linear regression, this accuracy reduced to 1 °C and 0.5 °C for TM and TIRS sensors, respectively. This indicated the proper performance of the regression approach presented in this study for temperature modification.

The present study seeks to identify and prioritize the factors affecting the formation of recurrent migration in the villages of the central part of Dezful. This research is descriptive-analytic in terms of purposefulness, applied and methodologically. Documentation and fieldwork have been used to collect information. Sampling method in this research was quotient sampling and questionnaire filling in a simple random sampling. In order to analyze the data, SPSS software and fuzzy inference system have been used in MATLAB environment. The results of simple t test showed that from the viewpoint of recursive immigrants, the economic and social index is the most important factor affecting the formation of return migration. The results of the correlation suggest that there is a strong and direct relationship between services and the number of returning migrants between villages. Also, the results of linear regression show that in the migration zone, the variables of age, sex, local belonging and residence distance are effective. Based on the Friedman test, access to infrastructure, communication, welfare and administrative services, land and housing in the village, and retirement occupations have been more important than other variables. Finally, the results of the fuzzy observation system indicate that if the value of the economic effect of 3.49 and the social impacts are 3.45 and the environmental impacts are 1.7, then the formation of the return migration would be 3.6 and the villages studied in the immigration Back up.

Cities with all the new facilities, are not capable of providing safety & peace for citizens, like in the past. therefore, the attention to the concept of citizen safety and its promotion methods has become one of the main priorities of urban professionals and theorists. New environmental design approaches emphasize the fact that, paying attention to the physical structure of urban spaces and application of special rules in them, a wide range of crimes can be prevented. The CPTED approach can be considered as a kind of targeted approach to the efficient and intelligent design of environmental components in crime reduction, which effectively affects social capital increase, improving quality of life, increasing citizenship satisfaction, and improving social welfare of the community. This research seeks to assess the feasibility of implementing the CPTED approach in Islamic cities. In the present study, the indexes of Islamic city and CPTED approach are measured in Tabriz municipalities using Pearson and regression tests. The calculations indicate that there is a meaningful relationship between the indicators of the Islamic city and monitoring public spaces, supporting the development of social activities, controlling of visual and physical access to urban spaces, paying attention to the hierarchy and the scope of urban spaces are most important in applying the CPTED approach in the Tabriz municipality regions.