The purpose of this study was to climate regionalization precipitation water atmospheric Iran. Therefore, the pressure data and specific humidity of the NCEP / NCAR database during 1950-2010 dependent of the United States National Atmospheric and oceanographic organization was extracted and analyzed. For the calculation used software programming environment of Matlab and Grads software and Surfer software used to perform graphics operations. In the study for the perceptible water climate regionalization use from cluster analysis and in order evaluate the results, used of discriminant analysis. In the data archive mean and Coefficient of Variation precipitation water month review and analysis the method cluster analysis ward composition, than dendrogram graph recognized three regionalization climates: a) climate regionalization whit high precipitation water, b) climate regionalization whit median precipitation water. b) Climate regionalization whit low precipitation water. The result archives from discriminant analysis indict that 98.24% from of pixels doing so as true in relative group.

One of the most important environmental factors affecting agricultural production, climate and small-scale man Spending it can control costs is costly.So the only way to tackle climatic limits the cognitive constraints in terms of coping with them and the crop is managed. The present study for corn cultivation Zoning in Lorestan province, the climatic elements of temperature, relative humidity, and degree days of the ten synoptic stations in the Lorestan province additional environmental parameters were used for slope and soil PH. Among the important factors in temperature due to its more importance in degree of phonological form was investigated, then the zoning maps for the production of these crops GIS techniques were used. After the initial production of maps for definition matched layers and influential areas of fuzzy standard method were used. Finally, in order to integrate data based on the weight of the evaluation hierarchy (AHP) was used to map land prone Zoning corn planted in Lorestan province, respectively. Zoning results using these methods showed that 2.34 percent province, an area prone to these crops, 8.55 percent semi-prone, poor and 13.08 percent in 76.03 percent in province with poor conditions where results indicate is most desirable places in the West region, central and northern parts of the province are scattered.

To determine regionalization of climate in Yazd province, 42 climate parameters were selected from climatology and synoptic stations of the study area and a 15×15 kilometers geographical network was considered to investigate the selected elements in the study area. The elements were studied in each cells knot using krigging method. Reduction of data matrix using factor analysis, varimax rotation, and climate zoning by cluster analysis and ward method were the next steps of the research. The results indicated that 5 factors accounted for 92.83 percent of the variation. These factors included precipitation, thermal temperature, dusty, wind and humidity, which explained 34.70, 32.74, 11.92, 7.08, and 6.39 percent of variation respectively. The results of hierarchical method of cluster showed that Yazd province with 131575 km2 has 6 different zonations including arid and cold, arid and dusty, semi arid and cold, very arid and warm, very arid and windy climes.

Understanding the climatic characteristics of a region plays a key role in sustainable development. The Esfahan province is extensive; this factor has caused it variety climates. The regionalization climate of Esfahan has conducts with new methods of regionalization such as factor analysis and cluster analysis. The 25 variables have been selected from 12 stations for this purpose. The investigation of climate of province by applying factor analysis method indicates that there are 5 factors which control climate of Esfahan. These factors in important order are: precipitation, temperature, clear sky and sunlight, windy and dusty, warm and moisture import respectively. One of cluster analysis on five climate factors determined that four climatic regions exist.

Ilam province, located in the west of Iran, owning natural, cultural attractions and capabilities on attracting tourists, but existing capabilities and potentials have not been used properly. In order to facilitate tourism, suitable times and places shall be determined for tourists` presence in the region. One of crucial needs of tourists is considering regional status and suitable times of tourism. In this research, TCI regional tourism index as well as data from 5 synoptic stations of the province was used to evaluate tourism regional and regional tourism conditions of Ilam province. Thus, necessary statistics of seven regional parameters were exploited from 5 synoptic stations in a 14-year old period monthly. (1997-2010). A database was formed after statistics exploitation then, they were processed using TCI index. Then, suitable times and places for tourists appearance determined through moderating by GIS software, generating point data to surface ones and mixing the maps. The results of the research indicate that the province`s tourism index owns many varieties, i.e. north areas of the province bears suitable conditions in warm season of year while the condition changes in cold seasons but southern areas of the province have suitable status while north areas bear unsuitable conditions. Consequently, March, April, May, October, November months present more suitable conditions for tourists in the province.

In this research it was tried to regionalize Iran's climate using multivariate techniques, like to define special characteristic of each climate. To do this we first collected datasets consists of 123 climatic and geographic variables from 77 weather stations all over of the country. After complete the missing data, ten variables were selected out of 123 using Procrustes method. These selected variables used to grouping stations using factor analysis and cluster analysis with Ward's method. Results show that the first two factors contain over 88% of variation in the datasets. So these two factors considered as dominant factors. Factor scores after rotation calculated for 77 stations. These scores then used to as an input matrix (77*2) for cluster analysis. All of sites classified into seven groups with respect to obtained dendrogram. In addition discriminate function analysis used to assessing the performed climate regionalization of the country. Results show that over 92% of sites classified by correct manner. So it can be said that results of regionalization performed by mentioned method, can then be used to following various studies like agriculture (especially dry farming), irrigation and so on.        

Self-Organizing Feature Maps (SOFM) are a category of artificial neural networks with a special ability in pattern recognition and data clustering using attributes of data points. In this study, ability of Kohonen self-organizing feature maps has been assessed in regionalization of Aras watershed in order to perform regional flood frequency analysis. Results showed SOFM can be used as an efficient tool for data clustering and regionalization of watersheds. Furthermore the results showed that the cluster validity measure is equal to optimum value for regionalization of Aras watershed, when the number of regions is equal to 4. Also heterogeneity measures are equal to optimum values, when number of regions is equal to 2 or 4. However, when number of regions is equal to 2, largest regions can be formed.

The Sistan & Balouchestan province is extensive, this factor has caused it variety climate. The climatical regionalization of Sistan & Balouchestan conducts with new methods of regionalization such as factor analysis, clustering and autocorrelation. For this purpose selected 20 variables of 10 stations. To investigation on climate province with factor analysis method shows that 5 factors make climate of Sistan and Balouchestan. These factors to arranged atmospheric humidity, precipitation, temperature, sunlight, wind and thunder import respectively. In the south of province such as Oman coastal humidity and cloud are important factors to determination climate. In the southern Balouchestan temperature, sunlight and thunder determined characteristic of climate of this region.  Windy dusty factors are characteristic of the north - east corner. In the northern of Balouchestan precipitation and thunder are important factors to determination climate. The sunlight plays a basic role to making climate in the east part of province (Saravan). One of cluster analysis on 5 climate factors determined that exit five region climates. At the end, the province divided into three regions with autocorrelation methods.