multivariate regression

The aim of this research is to investigate the relationship between urban surface heat islands and air pollutants in Tabriz metropolis. Landsat 8 and 9 images were used to calculate land surface temperature (LST) and urban surface heat islands (SUHI) from Sentinel 5p images for air pollutants in 2023 in spring and summer. The results showed that the value of LST and SUHI in the studied area was in the range of 9.75 to 46.57, -20.48 and 9.07 degrees Celsius, respectively, and the value of SUHI was higher in spring than in summer. is The results of air pollutants, aerosol absorption index (AAI), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3) showed that the concentration of pollutants in the central parts South, southwest of the study area has been dense. The statistical relationship between air pollutants and SUHI shows a weak inverse relationship between these two variables with R2 equal to 0.003 to 0.14. Also, ANOVA analysis showed that the high value of F statistic (between 40.3 and 65.56) and the low significance level of P_value at 0.000 can be said that there is a significant relationship between these two variables. The high number of T statistic also indicates the significance of these two variables. The Pearson correlation results showed that the correlation of NO2 with SUHI with R equal to -0.37 and -0.42 in spring and summer, respectively, was higher than other pollutants.

Determining the extent of riverbank erosion and displaying it as a map in a GIS environment is effective in the optimal management of water and soil resources. The aim of the current research is to delineate the riverbank erosion zone from aerial photographs, perform regression modeling, and identify factors influencing the development of riverbank erosion using various spatial data including physiographic, hydrological, geological, and environmental data in different sections of the Balikhlouchai River in Ardabil Province. Initially, influential parameters in riverbank erosion in the region, including topographic, soil and land factors, hydrology, and land use changes, were calculated. Accordingly, the variables under study were obtained for four time periods: 1955 using aerial photographs, 1980 using TM satellite images, and years 2010 and 2013 using Google Earth images, and the effective extent of riverbank erosion was compared. Subsequently, multivariate regression analysis was performed using independent variables (including topographic, hydrological, soil erodibility, and land factors) and the effective extent of riverbank erosion as the dependent variable, using SPSS software, and suitable models were developed to estimate the amount of different riverbank erosion. Based on the results of regression analysis, environmental parameters such as slope of the river reaches, peak discharge, area under irrigated agriculture, concentration time, pasture coverage, and residential areas played a more significant role in exacerbating riverbank erosion.

Considering the risk of sedimentation in the watershed of Mehran city, factors and parameters influencing sedimentation and the amount of sediment load in this area were statistically analyzed. The purpose of this research is to model the relationship between the amount of sediment production using MPSIAC experimental model factors and suspended sediment load using geomorphic parameters and their relationship with sedimentation in the watershed. By a simple random method, the basin of Mehran city with four specific sub-basins and equipped with a hydrometric station was selected from the southern basins of Ilam province. In order to analyze the relationship between MPSIAC model factors and geomorphic variables with the sediment of each sub-basin, statistical multiple regression method was used. According to the results of the experimental model, the amount of annual sediment in the sub-basins of Mehran city, Tang Bajak, Asan and Gonbad was estimated as 19.4, 16.8, 2.7, 12.14 and in the whole basin as 15.8 tons per hectare per year. The results of investigating the relationship between factors in sediment production and geomorphic variables in the sediment load of sub-basins showed that the amount of sediment production had a positive correlation with geological factors, land use, current erosion status, river erosion and basin topography and was significant at the 0.001 level. Is. The amount of suspended sediment load has a positive correlation with the slope index, roundness coefficient, rainfall, roughness and basin area and it is significant at the level of 0.001, and the amount of annual suspended sediment load in the sub-basins of Mehran city, Teng Bajak, Asan and Gonbad respectively is 2 It was estimated 16.5, 14.5, 11.6 and 12.4 tons per hectare per year. Principal component analysis and cluster analysis were used to influence the factors and variables on the sedimentation rate of sub-basins. The results showed that the percentage of variance explained by basin use explained 25% of the variance of all research variables and in total three factors of basin use, current erosion status, geology and two parameters of roundness coefficient and slope coefficient were able to account for 86% of the total variance. Explain the research variables. Finally, although the combined model of this research considers many factors and variables related to the amount of sediment production and sediment load, it has more significant and accurate statistical results in the watershed system.

Evaporation is the process of transferring moisture from the earth's surface - the water bodies to the atmosphere. Recognition and evaluation of evaporation is an important part in calculating water balance and proper management of water resources. From a climatic point of view, several factors are involved in the evaporation process, the most important of which are radiation and air temperature, wind speed and relative humidity. Evaporation variability at different time scales, especially during the process of global warming trends, is one of the considerable manifestations of climate change. Investigating the decadal changes in evaporation and the associated effective parameters play an effective role in the medium-term planning and management of water resources. The main purpose of current study is to analysis the decadal changes of evaporation in relation with the effective parameters (temperature, humidity, and wind) on the parameter.

To this end, daily databases of evaporation, temperature, relative humidity and wind speed, with a spatial separation of 4 × 4 km, obtained from stations (synoptic, climatology and rain gauge) data interpolation for the period 1969-2018 were used. In order to check the changes of the studied variables, the spatial average daily distribution of each variable for the whole period and for the five decades studied was estimated and analyzed. In addition, multivariate standard regression was used to track changes in the role and importance of each of the three climatic parameters studied (humidity, wind and temperature) over the decades of evaporation. first, each of the variables is standardized in relation to their mean and standard deviation as follows:Then the following regression model was used for K the independent variable to justify the spatial variation of evaporation ( ):

In Zayandehrood catchment, with increasing altitude, decreasing temperature and consequently reducing annual evapotranspiration can be observed. As altitude decreases from northwest to southeast, climatic elements such as heat and evaporation increase and some others such as humidity and precipitation decrease. And the cold and humid climate is gradually giving way to the hot and dry climate. The lowest average temperature is observed in the northwest of the basin and mountainous areas And the temperature rises to the southeast of the range. Therefore, we see areas with lower temperature, less evaporation and vice versa Which can be deduced from the high correlation (0.9) of these two parameters. This relationship is reversed in the case of moisture. That is, the highest relative humidity is observed in the northwest of the study area; Towards the southeast, the humidity decreases. After temperature, the moisture element has the highest correlation with the evaporation meter (0.79). The relationship between wind speed and evaporation is lower but more direct than other parameters. In the western half of the study area, its amount is less and in the eastern half of the basin, its speed increases.Also, The results showed that the temperature parameter has a more important and effective pattern on the evaporation process than other parameters, but the share of this parameter has been decreasing since the first decade (1978-1969) towards thelast decade. Humidity have ranks second among atmospheric elements in terms of its effect on the evaporation process that converse to temperature plays an increasing role from the early decades to the final decade (2019-2009). The same trend is repeated for the wind and its role is increasing from the first decade (1978-1969) to the last decade (2018-2019).

In this study, an attempt was made to use multivariate regression, Analyze the relationship between these parameters and the evaporation parameter in the period 2018-1969 And determine the most important parameter affecting evaporation in the study area. Then, the changes of decades of the relationship between these three variables and evaporation were discussed. Based on the general characteristics of this basin, it was found that the temperature and wind in the end parts of the basin is higher than the northern parts. Similarly, the rate of evaporation is lower in the northern half and higher in the southern half Conversely, the amount of moisture in the northern parts of the study area is higher than the southern parts. The results also show that The most effective parameter in relation to evaporation is the temperature parameter. This relationship was significant at 95% confidence level in the whole basin. . Moisture of the second share and Wind has the last category in the evaporation process. Research findings also showed The role of temperature is declining from the first decade to the last decade and Conversely, the share of humidity and wind is increasing.

Today, ecological footprint is mentioned as an indicator for evaluating human activities in environmental issues. This index is a method promoted by the Global Ecological Footprint Network to measure the human demand on natural capital needed to support people or the economy. Therefore, the ecological footprint measures both the supply of natural resources and human demand in the environment. The purpose of this study is to evaluate the ecological footprint in the top tourist destination countries that have faced many changes in the number of tourists in the last decade. Data from the World Bank and Global Footprint Network databases have been used for the past 24 years (1995-2019) for this research. The statistical analysis of the variables shows that the amount of ecological footprint in China, Mexico, Turkey and France is equal to 6.63, 3.31 respectively; 3.22 and 3.11 and the lowest amount of ecological footprint is in England and Italy with an average of 1.45 and 1.99. There is the highest correlation (R=729) between economic growth and ecological footprint. In addition, based on multivariate regression analysis, a strong correlation (R=0.75) has been estimated between the independent variable (tourism development) and the dependent variable (ecological footprint).

Morphometric analysis of a drainage basins and river network plays an vital role in understanding the hydrogeological behavior of the drainage basin . Therefore, the current research was conducted with the aim of modeling flood and its relationship with morphometric variables using multivariate regression. The research method is based on the destruction of morphometric parameters and statistical analysis, including correlation coefficient and linear regression analysis. In this regard, first, the studied basins have been selected using ARC GIS 10.5 software, and the limits of each one has been determined. Then, their morphometric characteristics have been extracted. The Flood discharge has also been reconstructed in return periods of 2 to 500 years using easy-fit software. For modeling, first, the linear relationship between each morphometric variable as an independent variable with flood discharge as a dependent variable was investigated. After ensuring the linear relationship between variables and flood discharge, correlation between each variable and flood discharge was calculated. The variables with the highest correlation were watershed area, basin length, and surface flow length. Using multivariate regression, modeling was used for independent variables and flood discharge. The results show that as the return period increases, the relationship between independent variables and flood discharge increases, So that the correlation of the area with the return period of 25 years is 0.609 and with the return period of 200 years is 0.677.also, the proposed models have more validity for flood prediction using multivariate regression analysis in return periods above 25 to 500 years.

From the point of view of geo structure, the location of Khanmirza plain in the folded Zagros zone in the south of the Dena fault and the presence of piezometers protruding from the soil, the presence of springs etc. are signs of crustal movement on the surface. The purpose of this research is to use geological information, seismological information, and satellite images in order to obtain a view of the tectonic activity of the present era in the Khanmirza plain, as well as to simplify the displacement calculation, to evaluate the displacement of the earth's surface and the parameters affecting this displacement, and providing a suitable model for this plain. In this study, the displacement rate of the earth's surface for 8 years (2003-2010) was calculated using D-InSAR radar images and radar interferometry. The effective parameters of the DEM elevation layer, slope, slope direction, profile curvature, surface curvature, distance from The road, the distance from the fault, the density of the fault, and the earthquake's intensity were obtained from the GIS environment. Furthermore, multivariate regression in the SPSS environment presented the best model for this plain. In this environment, the 8-year displacement rate was considered dependent, and the rest of the parameters were considered independent variables. The results were challenged in the STEPWISE model. The results showed that among the 13 methods, the 13th method is the best regional model for calculating crustal mobility in this plain by providing the best correlation coefficient of 0.826, a determination coefficient of 0.682, an adjusted determination coefficient of 0.675, and a standard error of 99%. Moreover, the average movement in this basin is a 10 cm rise for 8 years.

Sedimentation is one of the most important issues in the watershed. Due to the problems caused by sediment, it is necessary to investigate the relationship between hydrogeomorphic variables affecting sediment production and suspended sediment load in the watershed. The purpose of this study is to model the relationship between suspended sediment loads with hydrogeomorphic variables of the basin and to extract geomorphic features and their relationship with sedimentation in the watershed. By simple random method, two cows and conifers, which include 8 specific sub-basins and are equipped with hydrometric stations, were selected. Statistical multivariate regression method was used to analyze the relationship between geomorphic variables with sedimentation of each sub-basin. The results of the study of the relationship between geomorphic characteristics and sedimentation of sub-basins showed that the amount of sediment produced was positively correlated with slope index, roundness, drainage texture, rainfall, unevenness and basin area and was significant at the level of 0.001. In order to influence the variables on the sedimentation rate of the sub-basins, the principal component analysis and cluster analysis methods were used. The results showed that the three factors of roundness coefficient, slope coefficient and drainage texture coefficient of the basin explain 44.62, 25.22 and 16.74% of the variance of all research variables, respectively. In total, the three final extracted factors were able to explain 87% of the variance of all research variables.

With their many functions, metropolitan cities have strong relationships with the peripheral areas and especially the villages of the penetration zone, and this has a profound impact on the economic, social, cultural, and even physical aspects of the villages. Economic growth of cities is due to their economic relations. The city and the countryside have the advantages of attracting rural employment, creating out-of-village job opportunities and rural-urban migration. The metropolis of Tabriz, in various historical periods, has played a strong transnational role from Eastern Europe to South and Central Asia, which shows its undeniable role in taking on some strong professional functions to perform this task. This interconnection between the metropolitan city of Tabriz and its sphere of influence, on one hand, extends the physical development of the city to its national metropolis, and on the other hand, the physical development of the surrounding villages and extensive user changes in these villages, along with widespread demographic, economic and cultural changes will be obtained. This issue is the main subject of the present research and its main objective is to monitor and evaluate the role of the expansion of the metropolitan city of Tabriz by urban-rural migrations and its impact on physical changes, in particular, land use changes in the urban areas. This study concerning its purpose is a descriptive one and as the methodology is considered is an applied research which has been developed based on the questionnaires used in the study and analyzing its items. To achieve the objectives of the study, we used the Cronbach, Kolmogorov-Smirnov, T-Student and multivariate regression tests. The results show strong correlation between migration and change indicators if more than 85% of land use variations are expected by the migratory variables.

The purpose of this study was to investigate the factors affecting housing prices in the Laleh Park district of Tehran. In this study, the data of all real estate traded in the first six months of the year 2016 was used in the study area. Information about the physical properties of the residential units trained is collected from the Real Estate Market Information System of Iran and is used to obtain information on the accessibility features of residential units traded using ARC GIS software. Multivariate regression analysis has also been used to investigate the factors affecting housing prices. The results of this study showed that the physical factors of housing are more effective than the access factors in the housing prices in this district . Among the selected features, the variables of residential area, parking, and skeletal type had the most positive effect on the price of housing in the area under study. On the other hand, some of the features, such as the distance from the residential unit to the nearest main street, the residential unit to the nearest educational user, the residential unit distance to the nearest health care provider, and the residential unit's age, had a negative effect on the housing price in the Laleh Park district .

In recent decades, the issue of the quality of the urban environment has become a specialized and public issue in societies, and people's attention has shifted from material goals to the nature of life in dysfunctional urban complexes. What is clear is that all urban problems are of an environmental quality component. In other words, many environmental problems will quickly turn into urban problems. For this reason, in recent years, environmental sustainability, quality of desired urban environment, quality of life, and healthy communities have paid more attention to the quality dimensions in urban environments. In this research, the quality of the urban environment in District 5 of Tabriz Municipality is assessed. The approach of the present study is exploratory (at both descriptive and analytical levels) and it avails of survey method of research. Using Cochran's formula, the sample size of the studied neighborhoods was calculated to be 380 people. Using one-sample t-test, the quality of the urban environment was measured and in the next step, using the hierarchical analysis method of multiple regression, the indicators affecting the quality of the urban environment were identified. The results of one-sample t-test showed that from the residents' point of view, the quality of the urban environment of District 5 of Tabriz Municipality is not at a high level and there is no high satisfaction with the quality of the urban environment of the region among the residents. The results of multiple regression analysis showed that at the level of physical-spatial characteristics, the indices of Access and Roads Organization have the greatest impact (B= 0.518) on the level of citizens' satisfaction. At the level of functional-service characteristics, the sub-index of social welfare services (B= 0.531) have the most impact on the citizens satisfaction. At the level of content characteristics, sub-indices of environmental health (B= 0.680) and (B = 0.627) have the greatest impact on their satisfaction.

Frost is one of the most important phenomena in climatology, which is caused by changes in temperature over time. The sudden occurrence of this phenomenon at the beginning and end of the cold period can be very dangerous for the agricultural sector. Therefore, the awareness of the frost time - occurrence has long been considered by researchers (Thom and Shaw, 1958; Rosenberg and Myers, 1962; Schmidlin, 1986; Watkins, 1991; Waylen, 1988). In order to manage the reduction of the effects of this destructive climate phenomenon on the agricultural sector and the exploitation of large regional environmental capabilities, it is necessary to notice seriously the detailed study of this phenomenon and its characteristics at the land level. And this will be costly and time-consuming. Therefore, with the purpose of preventing the last two factors and at the same time achieving managerial goals, it seems necessary to accurately zoning and recognizing homogeneity and non-homogeneity between different areas in a large area.  

In the first step, daily minimum temperature data were adjusted based on Julius day, and the averages of the five indicators including the day of the onset of frost, the day of the end of frost, the annual number of days of frost, the length of the frost season, and the length of the growing season were extracted. In the second step, the five indicators were modeled separately with three land-climate factors, namely altitude, longitude, and latitude of the stations, using multivariate regression models. To measure the accuracy of the obtained models, four basic assumptions were examined (). Using the regression models obtained for all parts of the province, the statistical indicators of the frosts were calculated and generalized to the points without stations. Finally, using the kiriging method, each of the five frost indicators of the province was zoned.  

The correlation coefficient of three variables, altitude, length, and latitude with different frost indices was obtained by simultaneously entering these three variables into the regression model. And four basic assumptions for measuring the accuracy of the obtained models were confirmed. The earliest occurrence of the first day of frost arises between September 21 and October 27, and in the mountains of northwestern Kurdistan, especially the Chehel Cheshmeh. The latest occurrence of the first day of frost also happens in the eastern lowlands of the province between October 17 and November 23. The earliest occurrence of the last day of frost arises between March 22 and 30 in the lowlands of southeastern and southwestern Kurdistan, and the latest happens between May 24 and June 1 in the high peaks of the west and northwest of the province, such as Chehel Cheshmeh Heights at an altitude of about 3173 meters, Ketresh Mountain with a height of 2592 meters, and Vazneh Mountain with a height of 2697 meters. The highest frequency of frost is in the mountains of the region with more than 196 days and the lowest frequency is in the eastern borders of the province with less than 72 days. The northwest mountains with 235 to 248 days and the eastern and southeastern regions of Kurdistan with 123 to 137 days, respectively, have the longest and shortest length of the frosted season. The longest growing season belongs to the eastern part of the province. The average growing season in this area is between 214 and 227 days. However, within this area, small sections that are lower in height have a longer growth period. On the other hand, the shortest growth period is in the western and northwestern mountains, averaging 116 to 129 days.  

The results show that the three factors of altitude, latitude, and longitude can determine between 72 and 95% of the changes in different frost indicators. These three factors explain the 95, 90, 88, 80, and 72 percent changes in the length of the growth period, the occurrence of the first day of frost, the length of the frosted period, the frequency of frost, and the last day of frost, respectively. The Coefficient of determination is 95% for the first day of frost and 72% for the last day of frost. It seems that other factors besides the three mentioned factors play a role in changing the date of the last day of frost. Therefore, based on the studies of Noohi et al. in 2007, Noohi et al. 2009, and Alijani et al. in 2014, it can be inferred that the end frosts of the cold period can be more than the type of the advection frost. In other words, the synoptic factors can play a more important role in the occurrence of the last days of frost and its variability. But the spatial arrangement of different frost indices in Kurdistan province indicates a western to the eastern arrangement in the values of different frost indices. This means that with more movement from west to east, the number of frost days as well as the length of the frosted period decreases, and as a result, the growing season increases. In accordance with these changes, the occurrence of the first day and the last day of frost also arose with many delays between the eastern and western parts of the province. A comparison of the maps obtained from this algorithm showed that this method can provide more accurate details of the frost indicators compared to the zoning that used only stationary data (Mianabadi et al., 2009 and Ziaee et al. 2006).

The prominence of Resilient Cities theory, presented in the joint chapter of Urban Planning and Disaster Management Knowledge, is a future-oriented approach to comprehensively look at all elements of community survival alongside a set of critical and controlling factors, especially human factors. Along this path, mathematical modeling enables the relevance and impact of components as well as the monitoring of the resilience foresight process. The purpose of the present study is to evaluate the existing resilience, determine the main factors and their effectiveness in promoting resilience of Isfahan metropolitan area by modeling.

The general method of research is descriptive-analytical and in terms of purpose, it is infrastructural and applied. To determine the dimensions and indices of measurement, and to further evaluate the resilience, 50 experts were interviewed using Delphi method and then data were analyzed using SPSS software and R programming, and results were presented in the form of "Multivariate Regression Model. which was applied for six dimensions of resilience and the whole city.

Surveys show high and direct correlation between indicators of “social”, “cultural”, “physical-spatial”, “natural environment”, “economic” and “institutional-legal” resilience, based on correlation analysis. Isfahan metropolitan resilience of 2.87, in the range of five levels, is almost average.

The highest level of cultural resilience occurred at 3.08 and the lowest at 2.56 in the economic domain. Therefore, the first priority of planning resilience upgrades in terms of quantity is the "economic" domain and also the "institutional-legal" dimension of 0.200 given the level of effectiveness in the "Resilience Upgrading Regression Model", and the last priority in these two observation is 'cultural dimension' with respect to quantity and 'natural environment' with an impact factor of 0.147.

Variability is one of inherent properties of the climate system. In addition to the dynamic nature, the climate system is extremely intertwined nature also, so that its components interact with each other and eventually they change each other.. In general, the climate of a region or geographic location is controlled by various factors, including Atmospheric Action Centers (AAC's). AAC's behaviors have an effective role in short and long term changes in weather and climate conditions and also their components. AAC's can change the climate system. A climatic index is defined here as a critical value that can be used to describe the states and the changes in the climate system. Changes on climate are much slower than on the weather, that can change strongly day by day. Each climatic index is based on certain parameters and describes only certain aspects of the climate, so there are a variety of climate indices that have been defined and examined in numerous publications. For each climate index there is a defining equation that uses the so-called climate elements. These are measurable parameters that influence the properties of the climate system, primarily, for example, atmospheric parameters such as air pressure, air temperature, precipitation and solar radiation, but also non-atmospheric parameters such as sea surface temperature or ice cover.

Introduction A major issue has been the role of large floods in comparison to more frequent floods with lower magnitude. This work addresses the geomorphic response of Mountain Rivers to extreme floods, exploring the relationships between morphological changes and controlling factors. In October 2015, following the occurrence of extreme and sudden rainfall, a large and devastating flood occurred in Ilam province. The floods caused major changes in the morphology of Ilam's rivers and it was accompanied by a considerable extension. The rate of channel expansion in different sections of the studied rivers varied, which led us to examine the influential and controlling factors that led to the diversity of river behavior.The working hypothesis was that explanation of geomorphic effects requires models that include other variables (e.g: lateral confinement, degree of sediment) besides hydraulic related variables (cross-sectional or unit stream power). The main aim was thus to explore the relationship between channel widening and arrange of controlling factors.Other specific aim addressed were which channel width (i.e: pre- or post-flood width) should be considered to calculate unit stream power in order to have a better explanation of channel response? Since few studies have been done in this field, this research was conducted with the aim of investigating the factors controlling the response of Mountain Rivers to extreme flood events in upstream of Ilam dam. Materials and methods The research was conducted on three tributaries of the Konjancham River (upstream of Ilam dam) whose catchments were affected by an extreme flood on 7th October 2015. An integrated approach was deployed to study this flood, including (i) Analysis of channel width changes by comparing aerial photographs taken before and after the flood, (ii) Estimate of peak discharges in studied reaches, (iii) Determine the degree of sedimentation in studied reaches. Delineation of spatial units was carried out according to the approach proposed by Rinaldi et al. (2013), which is a modification of the approach by Brierley and Fryirs (2005).According to that approach, stream sectors were defined as macro reaches having similar characteristics in terms of lateral confinement, while reaches are homogeneous also in terms of channel morphology (channel pattern, width, slope) and hydrology. We used the reach scale (reach length was commonly from 200 to 1300 m) for an overall assessment of magnitude of channel changes and for a preliminary investigation of controlling factors. The dominant process observed in the study reaches was channel widening, which was analyzed in detail by comparing aerial photographs taken before and after the flood. To assess changes in channel width, channel banks, and islands, these features were digitized on pre- and post-flood orthophotos. Channel width was calculated dividing channel area by the length of the reach, and changes in channel width were expressed as a width ratio (channel width after/channel width before the flood).The estimation of peak discharges used to calculate cross-sectional stream power and unit stream power. The last part of the methodological section deals with statistical analysis carried out to explain channel response to the flood event, by exploring the relationships between changes in channel width and controlling factors. Results and Discussion The relationships between the degree of channel widening and possible controlling factors were explored using multiple regression analysis. The analysis was carried out for the widening (width ratio) at reach scale.The whole data set includes 38 reaches. Seven controlling variables were considered (confinement index, percentage of reach length with artificial structures, degree of sedimentation, channel slope, cross-sectional stream power, and unit stream power calculated using pre-flood and post-flood channel width), but each regression model incorporated only three to four variables. Each model included only one of the variables expressing potential or flood flow energy (channel slope, cross-sectional stream power, unit stream power). All four multiple regression models turned out to be significant (p< 0.001) and gave high coefficients of multiple determination (R2and adjusted R2 ranged between 0.73 and 0.8 and between 0.69 and 0.77, respectively). The best model was the one including unit stream power calculated based on pre flood channel width and confinement indexas explanatory variables. Conclusion Results confirmed the main hypothesis of this work that hydraulic variables alone are not sufficient to explain channel response to an extreme flood event. The inclusion of other factors, specifically lateral confinement, degree of sedimentation, and percentage of reach length with artificial structures, led to satisfactory models explaining the observed variability in the degree of channel widening. These results suggest that the widening process is essentially controlled by two factors: flood power and valley confinement. Notably, flood duration above a critical threshold was not included in our analysis, but it is a variable that very likely would increase the robustness of regression models in these reaches. The analysis carried out in the three subcatchments of the Konjancham River basin showed that unit stream power calculated based on pre-flood channel width has stronger relations with channel widening in comparison to unit stream power calculated based on post-flood channel width and to cross-sectional stream power. Because peak discharge was used for stream power calculation, we are aware that neither pre-flood nor post-flood channel width is actually appropriate for the estimation of unit stream power, as the most appropriate would be the (unknown) width at the flood-peak time. The fact that using the pre-flood width gives better relations with the degree of channel widening (width ratio) could suggest that most width changes occurred after the flood peak.

At present, one of the most important urban damage to cities is due to visual disturbances. On the other hand, security and urban perspective are words that although both are subsets of urban science and landscape architecture, there is a need for a closer examination of the relationship between them. While numerous views and experiences show that many of the criteria and measures taken in various studies that lead to the sense of security of citizens are directly or indirectly related to the principles used in urban landscape design, Reduce or increase. In other words, the urban landscape should be a safe haven for the lives of citizens. The present paper seeks to assess the role of the landscape in securing citizens' sense of security based on the model and coefficients of importance of criteria by carefully selecting the specific criteria of urban security - which is derived from previous studies and related to the city's landscape and the city aspect. Finally, Planning strategies and general points derived from the research model to enhance the role of urban landscape in providing citizens with security. Multivariate regression tests and single sample t-test were used. The results showed that memorable variables, location identity and sense of place had the highest impact on the sense of security in the neighborhood, with numerical value of "425, 408 and 398."

Housing is a basic need in the community always. Housing market has been one of the most fluctuation sectors of the economy of Iran country over the past years. Since the housing sector changes have a great impact on other sectors of the economy, so one of the significant needs of housing is predicting the price of this good. In this context, in this study by using of multi-layer perceptron neural network, presented a model to predict housing price in the city of Ahvaz and the results compared with the multivariate regression model. This study is a practical–developmental and its method is analytical- descriptive. To do this, 233 samples of statistical unit in 1392 were analyzed on the basis of 16 relevant variables. The results show that multi-layer neural network with 91 percent accuracy have been more accurate compared with the multivariate regression in the predicting housing prices. In addition to evaluating the performance of models coefficients R^2 and RMSE were used. Coefficient of determination (R^2) by using multivariate regression is .789 and its value for neural network is .918. The result of the regression model indicates weaker performance of this model compared to artificial neural network approach.

1. Climatic variation is one of the inherent features of the climate system. The components
of the climate system are diverse and complex, so that these components interact with
each other in a Interweaving way, so that the change in each component eventually
changes other components as well. The climate indicators are defined to describe the
status of the climate system and its changes. Each climatic index describes some aspects
of the climate based on certain parameters. Therefore, various climate indicators have
been proposed and used in many studies.
Climatic indices are measurable and computable and correlate with some of the elements
of the climate in different regions. Some atmospheric variables such as pressure,
temperature, precipitation and radiation, as well as non-atmospheric parameters such as
sea surface temperature (SST) or ice cover, are among the factors to be considered for
climate forcing in different parts of the world. The large water resources, such as seas and oceans, are among the most important climatic operators. These resources are capable of storing a large part of the solar energy and also, due to their fluid nature, are capable of transporting energy to other parts of the planet in various ways (surface flow, subsurface flow, convection, and moisture advection). Changes in ocean behavior, therefore, cause changes in atmospheric patterns, which can further change the short and long-term climatic conditions in different regions. For this reason, ocean surface temperature can be considered as one of the important indicators affecting climatic abnormalities.
All patterns of teleconnection as natural phenomenas, are resulting from the turbulent
nature of the atmosphere and its internal energy resources. These patterns represent
macro-scale variations in atmospheric wave patterns and jetstream flows, and affect the
distribution of temperature, precipitation, storm paths, and the status and pattern and
speed of the jetstream in large areas. For this reason, the patterns of teleconnection lead
to abnormalities that occur simultaneously in very distant areas (Asakere, 2007; 48). In
fact, the variability of the behavior of the atmosphere is a result of the set of behaviors
and interactions between the ocean and the atmosphere. Hence, indicators that explain
the abnormal behaviors of the ocean and therefore the atmosphere can help to identify
the causes and nature of the occurrence of short and long-term climate abnormalities in
a region. The study of air temperature anomalies in the warm season in Iran in relation
to the most important oceanographic and atmospheric indices is the main aim of this
research.
2. Material and In this study, two different databases were used including the data of the IRIMO stations
and indexes data of oceanographic and atmospheric teleconnection of the NOAA Data
Center, affiliated to the U.S. Center for Oceanography Studies. The data of the IRIMO
stations consist of 30 synoptic stations with a period of 50 years of data (1961-2010). In
the first step, the standardized temperature of each station was calculated per each month during the warm period of the year (from May to September). Then, for the purpose of detecting anomalies, a function was defined in Excel macro as; -0.5 >x> .5, and from among the 250 months examined the anomalies (at least 20 stations from 30 stations), 57 cases with anomalies among whole months were selected in the study period, and then by the Pearson correlation method, a relation was calculated between the 17 selected atmospheric-oceanic indicators and the air temperature. The indicators used in this study are the most important indicators introduced in international studies. Also, by using multivariate regression, optimal parameters and regression functions are presented in order to explain and predict the relationship between indices and temperature anomalies in the warm season in the whole of Iran.
3. The air temperature of Iran shows a significant relationship with the teleconnection
indexes. According to the tests performed in selective stations, in general, NINO3,
NINO1, NINO3.4, NINO4, GBI, CAR, PACEFIC WARM POOL and GLOBAL
MEAN TEMP indexes were have a significant correlation in 90% confidence level. In
terms of time in calculations with monthly synchronous steps at selected stations, the
best indexes are GBI, NINO1 2, NINO3 and NINO3.4, with correlations of 0.8, -0.8, -
0.57 and -0.4, respectively. In terms of a previous step, the GBI, NINO1 and NINO3
indexes had the highest correlation values of 0.8, -0.8 and -0.5, respectively. The
temporal pattern of the impact of some indicators, such as NINO, which was mostly
strong and inversely in the same month, was directly and significantly in the two and
three months earlier. Based on the results obtained from the multivariate modeling, the
correlation between the selected teleconnection indexes such as GLOBAL MEAN
TEMP, GBI, NINO 1 with thermal anomalies in the warm season of Iran are 0.94; as
the best temperature predictions, and at the same time a month earlier, the NINO3 index
was added to the above-mentioned indexes. In general, the indexes of NINO3-4,
NINO3, NINO1, NINO4, and GBI are the best atmospheric and oceanographic
indicators that predict Iran's temperature anomalies.
4. According to numerical correlation analysis between the selective indexes and the
temperature anomalies of the selective stations in the warm season in Iran showed that
NINO3, NINO1 2, NINO3.4, NINO4, GBI and GLOBAL MEAN TEMPERATURE
indexes are the most important oceanic-atmospheric predictors. Also, in this paper,
linear regression functions for the relationship between indices and monthly temperature
anomalies are presented, which can explain and predict the temperature changes in Iran.
The correctness of these functions is confirmed by using the actual and modeled data
(estimating R correlation values, RMSE and MBE values) with an acceptable error rate.
It should be noted as long as the intervals of predicting are prolonged, apparently the
importance of atmospheric indexes is reduced and contradictory the number and
reliability of ocean indexes are increased. In total, using the above mentioned indices
and using multivariate regression method in each step of time (simultaneously, one, two
and three months earlier), the linear regression function for the relationship between
indexes and monthly temperature anomalies of Iran has been presented, which by using
it the Iran's temperature changes can be predicted finally. It should be noted that the
functions obtained here are to predict the average temperature of selected stations in
Iran, and therefore for each station the calculations must be made individually.

1- Soil erosion by water is a dominant geomorphic process which threatens food security in most parts of the world .The geomorphic characteristics of a watershed play an important role in watershed hydrology, soil erosion processes and sediment yield. Geomorphic characteristics can be an indicator of soil erosion and sedimentation of a watershed. Geomorphic characteristics of watersheds are classified into linear, relief, and areal categories. Linear characteristics include stream number, bifurcation ratio, stream length and streams order. Relief characteristics include three-dimensional features of the watershed such as hypsometric integral, ruggedness number, and relative relief. Areal characteristics encompass morphological characteristics such as drainage density, stream frequency and watershed shape parameters. Accessibility to Digital Elevation Models and remote sensing data as sediment yield predictors simplify the calculation of the watershed geomorphic characteristics. The purpose of this study was to use the latest capabilities of geographic information system to extract the watershed geomorphic characteristics and determine their relationship with sedimentation in the subwatersheds of Khorasan Razavi province.
2- This study was conducted in 22 subwatersheds in Mashhad, Neyshabour and Fariman watersheds in Khorasan Razavi province. In order to select appropriate subwatersheds, the hydrometric and rainfall data for hydrometric and meteorological stations were obtained from Khorasan Razavi Regional Water authority for the selected watersheds. Annual sediment load was calculated using sediment rating curve method. Physiographic and geomorphic characteristics including 30 geomorphic parameters were calculated for each subwatershed using Digital Elevation Model with spatial resolution of 30 m. In order to determine the relationship between geomorphic characteristics and sediment yield of the subwatersheds, a multivariate regression stepwise analysis was used. In the multivariate regression, the important geomorphic characteristics which affected watershed sedimentation were identified and based on those parameters, the best annual sediment yield and geomorphic characteristics equation were presented.
3- The subwatershed areas under study vary from 40 square kilometers for the Chakaneh Olya to 9339 square kilometers for the Hossein Abad subwatershed. The average annual sediment yield for the studied subwatersheds during the period of 30 years varied from 1026 tons per year in the Jang subwatershed to 274572 tons per year at Hossein Abad watershed. The subwatersheds of Kalateh Rahman and Jang had the highest and lowest sediment yield, respectively, with 317 and 5 tons per square kilometers. The relationship between geomorphic characteristics and sediment yield of subwatersheds showed that the annual sediment yield had a positive correlation at the 5% confidence level with form factor and annual rainfall. The results of this study showed that the watershed shape parameters including form factor, elongation ratio and shape index had high correlations with sediment yield with the pertaining coefficients of 76.8, 76.5 and 72 percent, respectively. Also, the correlation coefficient of annual rainfall with annual sediment yield was 73.9 percent. After rainfall and form factor, elongation ratio was the third parameter that had a high correlation coefficient (76%) with sediment yield. In addition, watershed shape index which was a function of form factor was correlated with sediment yield at 72%. Among these characteristics, the annual rainfall and watershed form factor were used in the stepwise regression in the final model and were selected as predictor variables for sediment yield. Study results showed that the annual rainfall and watershed form factor variables could predict 64% of the annual sediment yield of the studied watersheds.
4- Discussion & The results of this study indicated that there was a significant relationship between the geomorphic characteristics of the studied watersheds and annual sediment yield. Watershed form factor was a dimensionless index for flood flow and movement, erosion severity and sediment transport capacity of watersheds. This factor was a function of watershed area and length. The runoff and the amount of flood peak in bigger watersheds will increase the sediment yield. Many researches have reported a high correlation between rainfall and sediment yield. Arid climate and poor vegetation coverage in the selected watersheds were the main reasons for the high correlation of rainfall and sediment yield. Soil erosion and sediment yield would increase due to the high intensity and low duration of rainfall along with the scarcity of vegetation coverage and erodible soils in this region. Overall, the study results indicated that with the development of new technologies and the possibility of extracting different physiographic and geomorphic parameters of watersheds from a Digital Elevation Model, it is possible to present regional equations for the prediction of sediment yield using geomorphic characteristics that can be used in sediment control and Watershed Management Programs.

Introduction Precipitation is an atmospheric factor, its quantity and distribution vary considerably in different parts of the planet, and is one of the most influential climatic elements that has always been influenced by the climate. Its amount changes in time and place continuously.Knowing the temporal and spatial distribution of rainfall is a useful tool for understanding how non-uniform distribution of water resources and vegetation in each region.Precipitation occurs when the wet weather and the climb factor exist both in the region.In other words, the wet air must rise to a certain height so that it can reach the saturation point due to the subsequent cooling down, and in the next, the cloud produces precipitation.The absence of any of these two factors prevents the occurrence of precipitation. Rainfall variation is considered as a key factor in the structure and functioning of ecosystems, but its impact on scale and magnitude is much less than its spatial variation.The climatic element, especially precipitation, has significant changes in time periods.Therefore, the recognition of the element of precipitation as one of the two elements of the climate and its changes in different times and places allows the optimal utilization of the natural environment.The amount and spatial distribution of rainfall is a fundamental factor for decision making, design and evaluation of hydrological models as well as water management and planning.Temporal spatial variations have diverse and varied impacts on the management and planning of water resources along a water basin and nationally.Climate change is one of the factors affecting the change of water resources.Precipitation, as a highly variable element, has always been a concern for climatologists and waterologists as a fundamental factor in the blue balance. The extreme variability of rainfall along the time-space has a variety of study approaches.The purpose of this research is to identify the conditions of rainfall in the Mazandaran province. Therefore, the location of rainfall in this province was investigated.In this regard, identification of the effective factors of the occurrence of these rainfall in different seasons and their role in the province has been addressed and its results will be available as a scientific and practical solution. Materials In this study, for the purpose of identifying the rainfall in the province of Mazandaran, five years rainfall from 2006-2010 has been used from a total of 12 synoptic stations.Using extracted data from precipitation graphs, rainfall of more than 10 mm was extracted in the studied area.Then the data were categorized into four parts: spring, summer, autumn, winter and year; to create the database, they entered the SPSS and ARC GIS10 software.In the spatial analysis of the data, the semi-modification of these models has been used, which was calculated using ARC GIS10 software.The methods used in the zoning of Kriging and IDW models for fitting include: IDW with three potentials of 1,2,3, and the Kriging method with spherical, circular, exponential, Gaussian, and spherical models, which is performed with conventional Kriging technique.Also, for statistical comparison of models, root mean square error of RMSE, MAE, RMSE and their correlation coefficient were used.Then, optimal mapping based on multivariate regression was fitted based on the simulation method and the recursive method of six variables in rainfall generation including latitude and longitude, number of rainfall days, elevation, relative humidity and dew point temperature. The effects of these factors on rainfall in the province will be evaluated in different seasons and annual. Results&Discussion The results of the spring survey show that there were 5 stations out of 12 stations without rainy rainfall.These stations are located in the plain and in the mountain range of the region.The analysis showed that the correlation coefficient between variables is R^2= 967, which indicates a strong relationship between the set of independent variables and the dependent variable.85.8% of rainfall in the spring season in Mazandaran province depends on these variables. In the summer, only 2 stations in the province did not experience rainfall ranges, both of which were at high altitudes and include the station Alasht and Kyasar.Variables show a very strong relationship in summer with a correlation coefficientofR^2=0.995.Which is 0.99% of rainfall in the Mazandaran province depends on these six variables.The fall season is one of the high seasons in the province of Mazandaran province. Only one station (siahbisheh) has been registered from 12 storm rainfall stations.Estimates show that the six variables analyzed in this chapter with a correlation coefficient of R^2 = 0.983 represent a strong correlation.The results of the winter season show that all stations in Mazandaran province have rainfall, although it includes fewer days than the autumn season.All stations experience at least one day at Alasht Station for up to 7 days in Ramsar.The results of the analysis show that in winter, the correlation coefficient is R^2 = 0.996. Conclusion The method used in the zoning kriging and IDW models for fitting include: IDW with three possible 1,2,3, kriging with spherical model is a model of, circular, exponential, Gaussian and spherical - is the ordinary kriging technique was conducted to assess the accuracy of each map generated by determining the best model calculated health. Also, statistical models for comparing the square root error RMS, MAE, RMSE and correlation coefficient was used for the best model for zonation model IDW and ordinary kriging with the 1.3 was a circle. After extracting the optimal plan based on multivariate regression models and ENTER precipitation method Backward method retrograded create six variables, including the length and breadth of geography, number of days of rainfall, altitude, relative humidity and dew point temperature were fitted. Results show that the six-variable correlation of 0.97 in spring, 0.99 in summer, 0.98 in autumn, winter is 0.99 and about 0.99 per year., Which indicates a strong relationship between these six variables Showers Mazandaran province.