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The aim of this paper is climatic classification of Khuzestan province. For this purpose, we first used eleven-variable relationships of humidity, temperature and precipitation in synoptic stations of the study area. Then we formed a matrix with the dimention of 13*11 applying Kriging method for convert matrix dimention (616*11) and also for principal component analysis and cluster analysis inputs. In this research the principal component analysis was used for understanding the component details and the cluster analysis was applied for the climatic classification. The results show that there are five climatic region and four components in Khuzestan province. Climatic variety conditions and regional settingsd indicate that this province is near the sea and dry regions of Iraq and Saudi Arabian countries that extensively expand toward the heights of Zagros Mountains.

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Abstract Of considerable factors in the development of tourism industry of each geographic region are climatic conditions. The present research has examined tourism climate in domain of the country, northern provinces, surrounding Caspian sea (Golestan, Guilan and Mazandaran provinces), using Miczochofscky, index of touristic climate (TCI) within geographic information system (GIS) environment. Finally, the conditions of the tourism climate of study provinces were evaluated through cluster analysis. In performing the research, statistics and data from 14 climatologic stations within northern provinces were utilized. Preliminary analysis was carried out, using seven climatologic elements, and involved in the tourism climatic index as well as statistical software. Then, the values of TCI were derived from studying cities, using the relations of Miczochofscky model. At the final step, TCI values were put in the of geographic information system (ArcGIS). Using inverse distance weighted (IDW) for mean finding, TCI values were expanded to the provinces. The zoning maps were prepared based on a monthly scale for northern provinces. The results of this research show that on spring, the eastern side is more appropriate than western side regarding tourist climate. In summer, only Siahbishe city of Mazandaran province possesses ideal conditions for tourism. In the fall season, month of Aban is more desirable than other two months and two cities of Mansil and Muravetappe possesses the best conditions for tourism. Considering the tourism climatology no proper and ideal condition is observed during winter. Eventually, four clusters were identified for studying cities, using cluster analysis.

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In order to carry out statistical and synoptic analysis of the sultry at the northern provinces of Iran, temperature, relative humidity, and saturated water vapor pressure obtained from Meteorological Organization of Iran in (1992-2007) period.The intensity sultry index and the partial water vapor pressure were calculated for each day. According to the defined threshold, the sultry days were divided into four groups. Statistical analysis proved that the August is the sultriest month with the average of 83% sultry. The Noshahr, with 149 sultry days, has the highest frequency of sultry days. Totally, west coast areas have more intensity of sultry than east coast areas. In order to synoptic analysis, 63 days of high sultry lasting two days or more, were selected The daily data of sea level pressure and 500 geopotential height were extracted from NCEP/NCAR database with spatial resolution of 2.5 x2.5 degree and then a hierarchical cluster analysis was applied using ward linkage method. Finally,the most important synoptic patterns were extracted which related to high sultry days.On the surface map was observed that Pakistan low pressure at the southern part of the country, and occasionally this low pressure penetrate into the study area. In addition it had been observed that high pressure tongue of Black sea and high pressure of north and west Europe in the area. Moreover on the base of synoptic patterns at the 500 hp level indicated that STHP was dominated.

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Abstract Understanding the physical characteristics of any particular regional climate features can play major role in land use planning. Climatic zoning of each region to identify possible environment and to exploit them, and to know the limitations and hazards in order to anticipate, is essential. According to the environmental and religious diversity, of climate on the northern and north-west of Iran, in this study the climatic zoning of the area was carried out. For this purpose, the data from annual average of 18 elements in 34 synoptic stations in the climate region with a common 21-year period (1985-2005) were used. The methods of factor and cluster analyses were used for this study. The factor analysis with principal components method, 18 elements in 5 regional climatic factors were summarized. These factors, in order of importance the factors humidity-precipitation, temperature, wind, thunder and dust. A total of 93.35% of these factors explain the behavior of the local climate. After determining factor, using cluster analysis method based on the integration, and the measure of distance, as well as regional stations were grouped according to the operating characteristics. The station had parallels in a climate group and thus 10 different climatic areas in the North and North-west of Iran were identified.

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Looking at the issues on urban literature in recent years and written numerous and various articles and books in field of "quality of residential environment" indicates the focus and attention of urban thinkers and theorists to this concept. In this survey with the aim of desirable residential environment analysis in Esfahan city we tried to identify the desirable residential environment and the parameters affecting the quality of the environment in order to improve the quality of residential environments. Survey data collected from the questionnaires in municipal senior executives have been analyzed with the use of analytical-descriptive method, Topsis technique and cluster analysis with in SPSS software. TOPSIS technique introduced that Abshar, Nazhvan, Mehr Abad, Abbas Abadand Dashtestan respectively, as the top five residential environments from the vision of municipal senior executives in the Esfahan city. Cluster analysis results classify desirable residential environments in four groups according to the survey index. Among factors affecting the quality of the desirable residential environment, various factors influence this quality among which, cleanliness and hygiene residential environment was the most important factor and access to historical-cultural complexes are considered as the lowest factors. According to the affecting factors on the quality of the desirable residential environment, seven ways have been presented for improving the quality of residential environments.

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In this study to determine the natural seasons of the Southern Caspian Sea, data average for the maximum and minimum temperature and average monthly area for 13 stations in the range were studied for a period of 20 years (1986 to 2006). To analyze the data the harmonic and cluster analysis methods were used. The results of the data analysis show that in the south of the Caspian Sea the year can be totally separated in three main seasons: hot, cold and transitive. In most stations, each of the hot, cold and the total cold and transitive seasons is composed of four months, but the natural seasons in the south of the Caspian Sea do not match with the beginning of the seasons’ calendar. The number of the months related to the hot seasons for every station is about four or five months and is included the months June, July, August and September and the months related to the transitive season are included in some stations between two to five months and they are April, May, October, November and December. In the end the number of the months related to the cold season in stations were variable from three to five months which they are included in November, December, January, February, March and April. Although Zanjan the coldest and Gilvan the warmest are among the stations of the range under study, it is observed that there are two seasons in these stations and it should be noted that the seasons variation from hot to cold occur in more humid stations of a gradual coast and in the longer term, but the same event for the stations that are far from coast and are higher occur in a shorter period.

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Temperature is one of the most important climatic variables being effective in specification and distribution of other variables of climate and is one of the main components of the zoning and classification. One way to planning and management is based on the knowledge of the thermal properties and their coverage area. In this study, using cluster and discriminant analysis multivariate methods, temperature zoning in the North West of the country was done. Using Kriging, 720 monthly minimum and maximum temperature maps with 8×8 km dimensions were created. Finally by removing cells out of range, 2436 cells were obtained, and a matrix database was created in the R form in 2436 rows (cells) multiplied 24 columns (variables). By applying cluster analysis four different temperatures in the North West area using Euclidean distance and Ward methods was identified. Discriminant analysis was used for classification and testing the accuracy of the cluster analysis. Based on the results of discriminant and cluster analyses, the groups differ only in %1.6 of the area. This result of the cluster and discriminant analyses also show that there is not a very commanding difference. This can be confirmed with the results of clustering. Each thermal zone was named on the basis of the comparison of each region with Iran’s average temperature (18 T^◦C). These temperature areas are: Very cold area, cold area, semi-cold with cooler day and warmer night and semi-cold with warmer day and cooler night. The identified geographic areas temperature patterns which are more consistent with altitudinal levels unraveled that cluster analysis can be considered as a useful tool for zoning thermal districts.

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Inequality and social justice long has been considered by social and humanity sciences and various schools of thought in societies. That is, spatial inequality as one of the important examples of social inequality is unavoidable in allocation of resources and facilities in urban communities. Therefore, what is to be paid attention by urban planners is measurement and affective factors on spatial inequality in order to prevent or to lead to a decrease in rifts between neighborhoods and urban areas. Based on this, present research is done with the descriptive-analytical methodology and with the aim of measuring and analyzing the spatial inequality between the areas of the city of Mahabad with emphasis on the rule of poor tissues and settlements in creating an urban spatial heterogeneous structure. In this paper by using the demographic, social, economic and skeletal indicators, the TOPSIS and the cluster analysis models, different areas of the city of Mahabad are rated and leveled in the year of 1393. Results and conclusions clearly show the unfavorable development of the city of Mahabad. But this unfavorable development doesn't only link to poor and old tissues and settlements of the city. Also, it has been found that not only resources and services are not distributed based on population but vice versa is right. Considering the deep gap between urban areas in skeletal indicator, it can be inferred that the issuing and prioritized problem in the unbalanced spatial structure of the city of the Mahabad is the skeletal factor in which the urban services and facilities is in the top which this has roots in the weakness of performing and exercuting the urban projects.

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In this study, to investigate the tourism climatology in Tabriz used two methods to identify weather types and TCI Index. Data base have been created with using daily data for 22 climatic variables including: dry and wet temperature, relative humidity, daily mean maximum and minimum temperature, daily rain, speed and direction wind in during 1981-2011 in Tabriz(For 30 years). Considering the goal of the research, which was weather typing recognition, matrix was prepared whit "P" decoration. "P" is decoration of data base matrix in which the rows are time and columns are the indicators of climatic variable. A cluster analysis which "Ward linkage" and "Euclidean distance" carried on standardized data and six synoptic types were identified: frost, fog and calm moderate windy rainfall-cold warm and dry and hyper hot and dry types. The results in research showed that the transition from one season to the other weather types will rise and mortality. On the other hand, during the last decades, significant changes in the weather types have been observed annually. The results show that the Tabriz tourism climate index, the best time for tourists to thermal relaxation time of maximum activity is the late spring and throughout the summer and the worst time is in the January and February.

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In this Research, the main synoptic patterns affecting snow with an environment-to-circulation approach in Kermanshah station in the West of Iran have been studied. To this end, the daily data of this snow station from January 1951 to December 2004 was collected from Iran Meteorological Organization. Also, to identify patterns of climate, sea level pressure, and temperature data, daily averages of temperature and pressure set of 500 HP data daily mean the reanalysis database by the United States Environmental Forecasting Center on the same date was collected, clustered, and categorized to analyze the temperature and pressure data on atmospheric patterns. Then, by analyzing the maps of all days the number of clusters was determined so that three of them justified the snowfall patterns in the region in the best way. Finally, to identify the most important systems, the combined patterns of sea level pressure and temperature and height and temperature level of 500 of each cluster were drawn and analyzed, forming the basis of this research. The results showed that snow in the west of Iran is affected by different systems near and far in the area, that is 1- formation and strengthening the European high pressure in northwestern of Iran 2- area located on the Mediterranean trough 3- the spread of Polar Vortex of the Southern latitude 4- Strengthening the Siberian High and the Himalayas in the Northeast and East of Iran 5- spread and penetration of Low pressure Sudan to the west of Iran. Consequently, the snowfall of these patterns can be classified in three clusters.1- low snowfall 2- moderate snowfall 3- heavy Snow.When these systems are less severe and extended, low and sporadic snowfall when strengthened, moderate snowfall and when they are at the peak of their activities, heavy snow in this area is witnessed.

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One of the major steps in regional planning is recognition of economic, social, cultural, and political inequality as well as other development aspects in different regions. The objective of this study is to analyze and examine the development trend of different cities of the East Azerbaijan province as possessed development indicators between 1997 and 2012. The research is a descriptive one and in terms of data collecting it is a documentary research. Weighted numerical taxonomy method and cluster analysis were applied for classification and also standard deviation for analyzing the development trend of the East Azerbaijan over the mentioned period. The average score of numerical taxonomy of province’s cities showed that facilities and services did not develop enough to fit the population in various areas of the province. Standard deviation of numerical taxonomy scores have reached from 0.136 to 0.083 which indicates a decline both in development gap and in inequalities among cities of the province.  The redults show that cities of Tabriz and Maragheh are the most developed cities while Khoda-afarin and Charavimagh the poorest cities of the province. In general, cities of the the eastern half of the province are more developed than those of western half of the province. Regarding development downward trend among cities of Azerbaijan province since 1997 to 2012 and also a decline in inequalities level in the province it is said that a decline in inequalities amongst cities led to a decrease in the degree of development throughout the geographical scope of the province. Continuity of this trend will intensify the deprivation crisis throughout the development process of the province.

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Flooding is a natural hazard. This phenomenon carries significant importance for Iran which is one of the world's six countries in terms of accident. Synoptic conditions of atmospheric circulation patterns are very important in identifying the risk factors that lead to flooding in heavy rainfall. This study used Morghak hydrometric station's daily and hourly discharge statistics as well as  daily precipitation data from the rain gauge stations and surrounding area during the period of 1360- 1388. Flooding date in the basin was extracted in order to study the flood-causing system in BAZOFT basin. Then, days of widespread rainfall in the questioned territory which synchronized with the occurred flood date were determined. Afterwards, four main patterns were specified through a cluster analysis on Euclidean distance of flood data in 487 sea level pressure. Thus, these four patterns were analyzed in terms of sea level pressure, geopotential height at 500 and 700 hPa, wind components, moisture flux convergence, and Omega. The results of synoptic maps analysis showed that when floodwater occurs, low pressure tab of the Mediterranean and Sudan spread toward the south-west of Iran and BAZOFT basin. The Mediterranean, Black and Red seas have played a role in strengthening the aforementioned tabs. Positioning of western wind through axes with cut off low pressure in this basin is the main factor of severe inconsistency and heavy rainfall. The results of the analysis of the convergence of moisture flux showed that torrential rainfall was mainly a result of water flow from the Sea of Oman, the Persian Gulf, the Mediterranean and the northern half of the Red Sea to the BAZOFT basin and moisture accumulation in the basin.

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Introduction
Investigation morphological of rivers is essential for understanding current conditions and the potential of possible changes of the river in future. Measurement of the geomorphic indicators is one of the methods that can provide the extremely helpful for understanding these changes. In the basin of Talvar the existence of the beds changes has created the issue of research.
Study area
The area of the river basin Talvar in Kurdistan province, with a total area of ​​7,241 square kilometers, is geographically located between 48˚ 06  ́53 ̋   to 48˚ 12  ́ 48 ̋  East and 34˚ 54  ́ 20 ̋ to 36˚ 00  ́ 10 ̋ North. This basin is one of the sub-basins of Ghezel Ozan drainage basin, located at the southern end of the basin, east of Sanandaj.  The Talvar drainage basin is one of the sub- basin of drainage basin of GizilOzan that is located at southern part of this pool and north east of Sanandaj.  In this study, due to the wide extent of the basin, and also to get more accurate results, the basin of Talvar was divided into 15 sub-basins.
Materials & Methods
In this study some of important indexes are calculated which included: Sinuosity index (S), Shape basin index (Bs), Asymmetry factor of a drainage basin (AF), Drainage density index (P), Branching ratio index (BR), Time of concentration (Tc), confluence angles.
For the calculation of the time of concentration was used the Kirpich equation. For measurement of the confluence angles was used the Digimizer Software. At the end, data was implemented  model of Cluster Analysis in the SPSS software.
Result and discussion
Check the value of the S: In the active tectonic areas river is in the form of direct line. If S is low and Closer to the number 1, is represents the tectonic activity in the area.  In the study area all of the sub-basins are equal to 1 or close to 1. So all are rated tectonic activity. The amount of S of sub basin number 12 is most of the others. Check the value of the Bs: The Bs value that is larger than the number 2 is indicating longitudinal basin.  Lower amounts of it is indicating weak tectonic activities.  So in the 1, 2,5,8,9,10,14,15 sub-basins, tectonic activity are high. 4 and 5 sub-basins are medium. 3, 7,11,12,13 sub-basins are weak.   Check the value of the AF: The AF indicator is one right way for determine tectonic tilting overload on the scale of the drainage basin. In a sustainable environment, AF should be about 50 that show the perfectly symmetrical drainage basin. So none of the sub-basins are not symmetrical. Check the value of the BR: BR in normal drainage basins is 3-5. So except for the sub- basin No. 1, all other sub-basins are normal. Check the value of the P: High density factor show active tectonic and the high sensitivity of the geological. The lowest value of the density related to sub-basin number 5. Check the value of the Tc: Usually the amount of Tc in a circular basin is shorter. But the stretches basin Tc is more.  Maximum value is related to the 1 and 5 basins. Minimum value is related to the number 3.  Check the value of the confluence angles: According to the calculations, 2 and 4 sub-basins has the highest average amount of angle and 5 sub-basin has the lowest.
Conclusions
Dendrogram corresponding to s shows that 12 and 13 sub-basins are single groups in terms of high s. and other basin are in a cluster. According to the dendrogram corresponding to BS 1, 8, 14, 15 sub-basins are in a cluster. 3, 12, 13 are in a cluster. 2 and 9 are in a cluster. And 4, 6, 7, 10, 11 are in a cluster. Dendrogram corresponding to AF shows that 1, 6, 14 sub-basins are in a cluster. 5 and 8 are in a cluster. 7 and 10 are in a cluster. 4 and 11 are single groups. And other sub-basins are also fitted up a single cluster. According to the dendrogram, 1 sub-basin is a single group Due to the high coefficient of divergence ratio. 14 and 5 that are longitudinal   sub-basins, are in a cluster. 2, 7, 15 are in a cluster and other sub-basins are in a cluster. The density dendrogram shows that 6 and 7 sub-basins that have a high permeability and multiple faults, are in a cluster. 5 and 12 are single groups in terms of relative poverty the density.  1, 4, 10, 13, 14 are in a cluster and other sub-basins are in a cluster. According to the dendrogram 1, 5, 15 sub-basins that are longitudinal sub-basins, are in a cluster. 2,3,10 are in a cluster. Number 7 is a single group. And other sub-basins are in a cluster. The average of confluence angles dendrogram shows that sub-basin 5 is a single group. 6 and 7 are in a cluster. 11,13,14,15 are in a cluster. 1, 3,8,9,10,12 are in a cluster. 2 and 4 are in a cluster. Accordingly, faults caused an increase collision angles. All indicators shows active or relatively tectonic in most sub-basins.

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Investigating and knowing the type of climate of a region and its dominant and effective elements will determine the climate of that region. The weather varies from one place to another because the weather elements and factors have their own conditions in each place. The lack of information about the sub-climates of the regions makes human economic and agricultural planning fail. In general, the climate of a region is the average weather condition in that area, and accessing the average weather condition in a specific place requires long-term meteorological statistics and information. In order to obtain a correct and comprehensive understanding of the climate of the Caspian region, climate zoning was carried out with new statistical methods such as factor analysis and cluster analysis during a period of 23 years (1990-2012). For this purpose, 26 climate variables were selected from 16 meteorological stations. Then, using the digital height model, a multi-variable regression relationship was applied between the meteorological parameters and the mentioned layer, and finally a zone matrix of 242 x 26 dimensions was obtained and was used as the basis for zoning. Investigating the climate of the region with the method of factor analysis shows that the climate of the province is made up of 3 factors, these factors in order of importance are: temperature, precipitation and wind-thunderstorm. The results of cluster analysis on 3 climatic factors show the existence of 4 regions in the Caspian region. The findings indicate that the first and second factors alone explain 84% of the climate behavior in the region.

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One of the important climatic events that has a great impact on the life of living organisms is extreme temperature events. Cold days and cold waves are examples of extreme temperature events in which unusual minimum temperature values are observed. The creation of these temperature conditions is a function of complex synoptic and dynamic patterns, the identification of which can be useful in increasing awareness of how these conditions are formed and predicting similar situations and thus reducing possible damages. In this research, based on the concept of relative coldness in time and place, cold wave was defined as a condition in which the minimum temperature standard score is less than -1.2; continue for at least 3 days and cover more than 20% of the country's area. In order to meet the objectives of the present research, two environmental (minimum temperature) and atmospheric databases (sea level pressure, geopotential height, atmospheric temperature and orbital and meridional wind component) were used for the winters of 1339 to 1394. The results of examining the patterns showed that all the cold waves in the country were caused by the formation of a high-pressure pattern on the surface of the earth. The arrangement of the two high-pressure systems of Siberia and Azores have played a very important role in guiding the cold air of northern latitudes towards the country. The location of sub-arctic low pressure in the northern regions of Europe and Russia has also caused the cold polar air to move to more southern latitudes. Mid-atmosphere patterns have also had a significant impact on the creation and continuation of cold waves. In such a way that the most severe, widespread and continuous winter cold waves were formed when the barrier systems were established in Eastern Europe and their eastern flank was placed over Iran.