جستجوی مقالات مرتبط با کلیدواژه « رگرسیون خطی » در نشریات گروه « جغرافیا »

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 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.

 Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction results in the creation of centers susceptible to dust storms. With socio-economic impacts ranging from urban to intercontinental and from a few minutes to several decades, this can challenge regional development. The first estimate of potential dust sources is derived from the soil properties. With the reduction of surface soil moisture and the wind speedcrossing a certain threshold level, wind erosion process can cause the formation of dust storms. Field studies have proved that increasing the moisture content in soil from zero to about 3%, reduces the dust concentrationsignificantly. To understand the climatology of dust and develop related numerical predictive methods, continuous recording of dust storms is essential, which requires effective and continuous monitoring of the variations in surface soil moisture. Remote sensing technology is an effective method for calculating soil moisture. This technology was first used for the estimation of energy flux and surface soil moisture in the 1970s. To extract the surface soil moisture content, some remote sensing methods use surface radiation temperature and some others apply water transfer (soil/vegetation/air) (SVAT) model. Various indices have been developed for soil moisture monitoring, such as soil moisture (SM), soil water index (SWI), Temperature-Vegetation-Dryness Index (TVDI), Soil Moisture Index (SMI) and Perpendicular Soil Moisture Index (PSMI), all of which combine vegetation and surface temperature variables.

Soil moisture is considered to be a significant parameter in the exchange of mass and energy between the Earth surface and the atmosphere. Lack of soil moisture or decreased moisture in soil is considered to be a factoraccelerating the process of dust storm formation. During the previous decades, water stresses on the ecosystem of Hour-al-Azim have transformed this wetland into one of the main dust centers in the southwest Iran. Hour-al-Azim is one of the largest wetlands in southwestern Iran. This wetland is shared between in Iran and Iraq. It is located between N 30° 58´- N31° 50´ and E 47° 20´- 47° 55´. The Iranian part of this wetland encompassed an area of 64,100 ha in the 1970s, while in the 2000s, the area has decreased to only 29,000 ha. The present study aims to monitor the spatial-temporal variability of soil moisture in Hour-al-Azim wetland and to investigate the relation between these changes and dust storms in the southwest Iran. To reach this end, we used 8-day images obtained from the Aqua satellite in the period of 2003 to 2017 and also annual frequency of dust storms with a visibility of less than 1000 m in the period of1987–2017. A database consisting of 189 images of the red band, near-infrared band, and ground surface temperature (LST) was created, which contained 4 images per year (one image per season). The resolution of the red / near-infrared band data and daily LST values were 231.65 and 926.62 meters, respectively. Then, soil adjusted vegetation indices (SAVI) and perpendicular soil moisture index (PSMI) were extracted. SAVI index is used to reduce the effect of background soil on vegetation cover in semi-arid and arid environments with less than 30% vegetation cover.Compared to NDVI, SAVIwith L = 0.5reduces the effect of soil changes on green plants. In the next step, a trapezoidal method was used to calculate the PSMI index. In order to investigate changes in the soil moisture content of the Hour-al-Azim wetland, three time series obtained from regional mean of SAVI, LST and PSMI remote sensing indices and a time series consisting of the number of days with dust storms observed in the 9 stations were evaluated using simple linear regression test. 

Extracting Soil Adjusted Vegetation Index indicated that in the study period, the highest values of this index was observed with a regional mean of 0.15 on 4/7/2014 and the lowest values was observed with a regional mean of 0.08 on 1/1/2005. Land Surface Temperature survey showed that during the study period, the highest values of this index was observed with a regional mean of 54.42 ° C on 7/4/2010 and the lowest values was observed with a regional mean of 17.28 ° C on 1/1/2007. The regional mean of Perpendicular Soil Moisture Index indicates that despite winter is considered to bethe wettest season of the region, PSMI index with a regional mean of 0.2 has experienced the driest soil moisture conditionsat the beginning of winter (1/1/2016),while it had experienced the wettest soil moisture conditionsin the same season on 1/1/2009 with a regionalaverage of 0.13. 

Finding of the present study indicate an increasing trend in the range of remote sensing indicators. The range of SAVI index is increasing, which means that the density of vegetation in the Wetland is decreasing. Perpendicular Soil Moisture Index values also show an increasing trend, indicating a decrease in soil moisture content. As a result of the decrease in soil moisture, the vegetation density also has decreased and the land surface temperature has increased. Results of statistical tests indicate the role of changes in environmental conditions of Hour-al-Azim wetland in the frequency of dust storms. Using findings of the present study, or studies such as Kim et al. (2017), it is possible to take advantage of soil moisture variations for the prediction of dust generation, its emission, and spread level.

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.

Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries.

Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed:  a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters.

The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).

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.

  River sediments are transmitted in two ways: either these substances are immersed in the flow of water, and they move with water, which is called suspended sediment, and the amount of suspended sediment that passes through a section of the river at a time They call a suspended load, or they move a slip, slide, jump, to which they say the bed load. Artificial neural network is a method that is based on the simulation of human brain function for solving various problems and the input, output, and median of the neuron layers and the weights associated with the input values ​​and the bias and the stimulation function. The study area in this study is the catchment area of ​​Golrood River.
Artificial neural network is a method whichwasprovided based on the simulation of human brain function for solving various problems and formed from the input, output, and median neuron layers and the weights associated with the input values and the bias and the stimulation function. One of the features of the artificial neural network can be referred to as the calculation of a definite function, the approximation of an unknown mapping, pattern recognition, signal processing, and learning (American Society of Civil Engineers, 2000). The disadvantages of neural network methods are that it does not provide a function which can be used explicitly. Many studies have not been conducted on sedimentation using a neural network (Govindaraju&Ramachandra, 2000; Sarangi & Bhattacharya, 2005).Feedforward error back propagation neural networks with nonlinear functions (sigmoid) has high flexibility and can be very effective in approximating a function, finding the relation between input and output, and so on. In hydrology, the use of these networks is highly recommended considering the turbulence dominating runoff-sediment data, (Flood &Kartam, 1994).
Javadi and others (2015) in an article compared river sediment estimation method using two methods of artificial neural network and SVM in Iran. Then, the output of these models was compared with the experimental models and eventually the RMSE and R indices to compare these models were used. The results indicated that SVM model has better estimation than artificial neural network model. The RMSE was 75 for this model.
Semkol et al. (2016) estimated the amount of sediment in the Shiwan River in Taiwan. In this study, artificial neural network model and sediment rating curve method were used. The results showed that the MLP neural network model was able to provide an appropriate estimation of the amount of sediment with R value of 0.97 (Tfwala& Wang, 2016).Afan et al. (2016) also estimated the amount of river sediment in the Johouw River. In this study, two models of neural network RBF and FFNN were used. Finally, it was found that the FFNN model showed a much better performance than the RBF model. The R index of this study for the FFNN model was 0.92 and its RMSE was 26, while the RBF model had R value of 0.86 and a RMSE of 32 (Afan et al., 2015).
Summarizing the research history showed that the static regression methods did not have high accuracy in estimating the suspended sediment load discharge.In recent years, the focus of predictive models has also changed from linear regression to neural network models. Most researchers have been providing comparisons between different models of the neural network during these years and also, in their final modeling, tried to use the domain morphology factors in the final model to improve the accuracy of the final model. Therefore, the use of artificial neural network method and considering the dynamic behavior of sediment suspension load and considering the flow of previous days as an effective variable has been evaluated in this research.

The study area
The study area in this research is the Gelerood river basin. This area is located in Borujerd, Lorestan province, west of Iran. The basin is between longitudes 48.30 to 48.55 degrees and latitudes 33.45 to 34.00 degrees. GeleroodRiver drains waters of an area of 70 square kilometers. The average height of this basin is 2350 meters. The river originates from a number of headwaters in the village of Vanai in the west of this city and receives other branch in the western part of the Boroujerd city in the vicinity of the Chogha hill from the north.
There are 8 stations named such as Doroud-Tireh, Doroud-Marbareh, DarehTakht-Marbareh, Vanai-Gelerood, Biatoun, Rahim-Abad, Water Organization and Chogha hill in the area of Silakhor plain in Dorood-Boroujerd area. In Gelerood river basin, two stations of Vanai and Water Organization have been used to estimate the amount of river sediment. The position of these two stations in relation to the Gelerood River and its sub-basins is shown in Fig 1.
Data used
In this study, the instantaneous flow rate- instantaneous sediment statistics recorded deposition related to the period 1971 to 2002 were used. These figures include the instantaneous daily flow rate per cubic meter per second and the instantaneous daily sediment per day that were measured simultaneously. Morphological characteristics of the basin including the area, length of the river and its environment using ArcGIS software and geomorphologic parameters of the basin using natural features of the basin have been calculated based on the guidelines of Singh et al. (2009) using the ArcHydro plugin installed on the above software.

So far, different prediction models have been used to estimate the sediment volume of rivers. Some of these models estimated the amount of sediment by combining various physical parameters of the domain, climate, and even satellite image outputs. Artificial neural network models are widely used today to predict geographic models. In this study, three models of artificial neural network RBF, artificial neural network MLP and multivariate linear regression model have been used to estimate river sediment.
After calculating the RMSE and MAE indices, given the lower the rate of these indicators, the predicted value is closer to the actual values, so MLP artificial neural network models have a better accuracy than the other two other models in estimating the region's sediment. On the other hand, according to the value of the index calculated for the three models, the accuracy of the model estimation is calculated 90.44 for the MLP model, the value for this model is 0.88. After the MLP artificial neural network model, the RBF artificial network model provides better results. In this model, the value of is 0.4, which indicates the estimate accuracy of the half of the MLP model. In the third place, the multivariate linear regression model with value is 0.3.
Two neural network models of MLP and RBF were also studied in this research. The MLP model was able to estimate sediment data with a better accuracy than other models. Thus, the feasibility of using feedforward neural network models in the estimation of sediment load can be confirmed. Based on the available time series, more accurate estimates require long periods of time, as well as considering climate changes in this research can help improve the results and accurately predict the amount of sediment. On the other hand, taking into account the soil type-specific parameters of the area and the potential for water penetration in the soil for each sub-basin can be effective in improving the results. The results of this study indicated that there is a significant relationship between the amount of suspended sediment production with the number and severity of runoff events. Among the physical characteristics, the area of the basin and the length of the main river are other factors that affect the estimation of the river downstream sedimentation rate.
As well as, recurrent neural network models can be used in the following studies, given that the stations are located along the other stations. Moreover, the combination of satellite imagery data can lead to more accurate models, given the fact that this data is also available to users from past periods