| چکیده انگلیسی مقاله |
 Climatic Regionalization of Markazi Province: Application of Factor and Cluster Analysis      M. Khosravi. M. Armesh  Received: May 25, 2011 / Accepted: November 13, 2011, 25-28 P     Extended abstract  1- Introduction  The climatic classification from the distant past has attracted the attention of climatologists. In traditional methods one or more climatic elements considered for classification but these methods cannot indicates the reality of climatic regions. Therefore in the recent years researchers have tried using the dominant parameters affecting climate and multivariate methods have provided a real images from climatic regions. The aim of this study is climatic regionalization of Markazi province by utilizing 29 climatic parameters and use the factor and cluster analysis. Combined use of these parameters in the climatic classification can improves accuracy and shows a real aspect of province. Recognition of microclimates can help us to identify the strengths and weaknesses of regions climatic characteristics and useful for development planning proposes.   2- Methodology  The 29 climate variables from 21 synoptic stations from province and adjacent areas were used. By using the statistics of adjacent stations, accuracy and resolutions of factors and climatic zones were increased. The statistical data were normalized and also, due to different scales of data, the standard scores were used in analysis. The factor analysis and clustering method were applied for classification. After estimation of stations factor loading scores, by using of IDW method, 5*5km nodes were created, using these nodes instead stations in classification improved the accuracy of climatic classification. Eventually by calculation of factor scores in stations, a cluster analysis was applied. For interpolation purpose the kriging methods in GIS were used.     2-1- Factor analysis  The factor analysis as multivariate statistical methods can reduce the number of variables. The advantage of this method is that not only reduces the number of variables, but also keeps the variance of main data.  If the internal correlation between variables is much closer, the number of emerged factors is to be less.   2-2- Cluster analysis  In this method, the grouping of observations based on their distances, this means that observations have short distances classified in one cluster. The aim of clustering method is construction some group that the within group variance less than between group variance. The distance method usually applied for two or multi criteria clustering.  In this method, Euclidean geometry was used for distances measuring of members. According to Euclidean distance between spatial and temporal points, the distance matrices to be created that based on these matrix distances, determined the spatial and temporal cluster.   3- Argument  The factor analysis over variables was showed that the 6 components explained about 90% of region climatic behaviors. The factors with regards to weight of them over the variables are named. These principle components are Dust-coldness, precipitation, Cloudiness-humid, Thermal, precipitation- coldness and Cloudiness - Thunder . The dust-coldness factor has its maximum weights over Arak region. In south west of province, the precipitation factor were dominate and the cloudiness-humid factor active over the north of province. The thermal factor was affected over Arak and some of southeastern regions of province. Precipitation- coldness factor in Tafresh and north of province and finally Cloudiness- Thunder factor dominated over North West and Taftresh area. The cluster analysis over these 6 factors confirmed 7 climatic regions in Markazi province.  These regions are:  The temperate and semi-dust  Dusty and semi humid  Warm and semi arid  Dusty Semi cold and semi humid  Temperate and dusty semi arid  Semi arid Temperate  Cold and dusty semi arid  Semi cold and humid thunder.    4- Conclusion  In the studied area despite the homogenous synoptic systems Because of vitiate geographic factors such as elevation, topographic orientation latitude and etc, the role of synoptic systems are overshadowed. These caused numerous microclimates in the region. The results of factor analysis shown that climate of region affected by 6 components. These principle components are Dust-coldness, precipitation, Cloudiness-humid, Thermal, precipitation- coldness and Coldness â Thunder. These components explained about 90% of region climatic behavior . Cluster analysis shown 7 different climatic regions. The factor-cluster analysis technique is found advantageous over many of traditional methods, as it produces richer regions and shows clear climate variations within this province.  Key words: Climatic regionalization, Cluster analysis, Factor analysis, Markazi province     Refrences  Ahmed, B, Y . Mohammad (1997). Climatic classification of Saudi Arabia: an application of factor â cluster analysis, Geo Journal, 41.1: 69â84.  Akbari, T, SA, Masoodian (2009). Identification and classification of thermal region of Iran, Geography and Development Journal,Vol 33,No 1,pp59-74  Alijani,B (2002).Synoptic Climatology,Samat publication  Azizi,G(2001).Classification of selected stations in Iran by using Litein Sky method, Journal of Geographical Researches, Vol 41,pp 39-51  Bishop,I.D(1984). Provisional Climatic Regions of Peninsular Malaysia, Pertanika 7(3), pp. 19-24.  Carbajal. N, L. Pineda Martinez, E. Medina Roldan(2007). Regionalization and classification of bioclimatic zones in the central-northeastern region of Mexico using principal component analysis (PCA), journal content, Vol 20, No 2, Universidad Nacional Autonoma de Mexico.  Derakshi,J(2010).Estimation and Classification of Solar radiation in horizontal surface by using climatic factors in GIS environment,Case study:East Azarbaijan province,Ms.c Thesis,Superviser:M.khosravi,University of Sistan and Baluchestan.  Dinpajou,Y,A.Fakeri,M.Moghadam,S.Jahanbakhsh,M.K.Mirnia(2003).Variable selection for Precipitation classification of Iran by multivariate methods, Iranian Agricultural Science,Vol 34,No 4,PP 809-823  Eslahi, M. (2003). The detection of Iran climate change by using cluster analysis, the third regional conference on climate change, IRIMO  Esmail Nejad, M (2005). Climatic Classification of Sistan & Baluchestan province by Using GIS, Ms.c Thesis,Superviser:M.Salighe, University of Sistan and Baluchestan  Gaitani, N. Santamouris, M. Mihalakakou, G. Patargias(2006), Cluster Analysis in Energy Classification of School Buildings, AIVC 27th conference - EPIC2006AIVC, Lyon, France.  Gerstengarbe F.W, P. C. Werner, K. Fraedrich.(1999). Applying Non-Hierarchical Cluster Analysis Algorithms to Climate Classification: Some Problems and their Solution, Theoretical and Applied Climatology, Volume 64, Issue 3/4, pp.143-150.  Ghayoor,H,M.Montazeri.(2004).Classification of Iranian thermal regimes by principle components and clustering analysis,Geography and Development Journal, Vol 4,pp21-34  Heise B, B. Bobertz, , J. Harff.(2010), Classification of the Pearl River Estuary via Principal Component Analysis and Regionalization, Journal of Coastal Research, pp.769-779.  Iranian Meteorological Organization.(2010).website: www.weather.ir  Jafarpoor, E. (1998). Climatology, Theran University Press, pp 400  Jahanbakhsh, S, S.Torabi. (2004).Forecasting of Temperature and precipitation variation of Iran, Geography Research Quarterly, Vol 74, pp 104-125  Kaviani, M.R, B.Alijani. (1998).The principles of climatology, Samt Publication,p 582  Khalili,A,A .Darvishsaft, R .Baradaran,J.Bazarafshan.(2004).A suggestion for climate classification in GIS,Case study :Northwest of Iran,Desert Journal,Vol 9,No 2,pp 227-237  Khalili,A.(2004).Designing a new system for Climatic classification from thermal-cooling requirements and its management over Iran, Geography Research Quarterly,Vol 75,pp 5-14  Masoodian,S.A.(2003).A survey on geographical distribution of Iranian precipitation by using rotated factor analysis, Geography and Development Journal,Vol 1,pp79-88  Masoodian,S.A.(2003).The climatic regions of Iran, Geography and Development Journal,Vol 2,pp 171-184  Masoodian,S.A.(2005).Identifying of Iran precipitation regimes by using clustering analysis, Journal of Geographical Researches,Vol 52,pp 47-61  Masoodian,S.A.(2009).Precipitation regions of Iran, Geography and Development Journal, Vol 13,pp 79-91  Masoodian, S.A.(2011).The climate of Iran,Sharia Toos publication,p193  Nasiri, R.(2008).SPSS stepwise training, Nashr Gostar Press ,Teheran,p 291  Newnham, R. M. (1968), A Classification of Climate by Principal Component Analysis and Its Relationship to Tree Species Distribution, Forest Science , Volume 14, Number 3, pp. 254-264.  Salighe,M,F.barimani,M.Esmailnejad.(2008).Climatic classification of Sistan and Baluchestan province,Geography and Development Journal,Vol 12,pp101-116  Singh C.V.(1999). Principal Components of Monsoon rainfall in normal, flood and drought years over India, international journal of climatology, pp.639-652.  Yunus, F.(2011). Delineation of Climate Divisions for Peninsular Malaysia, Geospatial World Forum, Dimensions and Directions of Geospatial Industry, Hyderabad, India.      |