| چکیده انگلیسی مقاله |
 Identifying homogeneous hydrological basins base on effective geomorphologic variants on flood by cluster analysis Introduction    H. Ataei. M. Shiran  Received: 31 August 2009 / Accepted: 16 February 2011, 17-20 P         Extended abstract  1- Intro duction  Drought and flood are events that humans are horrified about them and try to manage them. Today flood management by un-instrument management is considered and the first step for decreasing flood risk is watershed management in up-basins areas. Defining critical sub-basins by grouping in similar group in point of flood intensity and like geomorphologic variable is a useful method for flood management. In this way clustering method is used for identifying homogeneous hydrological basins.  Three main methods for clustering in statistical science are included : Hierarchical, Un- hierarchical and Fuzzy method.The number of researches has been done in Iran and other countries by these methods as following:    See and Openshow (1998) for increasing of flood risk forecasting in England and Roger ET all. (2000) for preparing of soil hydrology map in France used fuzzy clustering. Both conclusions indicated fuzzy method is more suitable in point of operation and low cost. In Iran Ghiyasi and ET all, (2005) used geomorphologic quantitative variance (such as area, perimeter, length and slope ( of main water way and watershed for indicating the homogenous sub-basins in northern Alborz. they showed fuzzy method no need to estimate many parameters to achieve fit result.   2- Methodology  Study area: The area in this research is a part of Karvan city in western Isfahan Province and a sub-basin of the big Morghab watershed (upward basin). Morghab River attaches to Zayaande Rood River. Karvan plain located in 50º 32â² E to 50º 56â² E and 32º 50â² N to 33º 2â² N. The area of basin is 326.189 km2 and its perimeter is 82.554m. The average height of basin is 2545.29m, highest point is 3640m (Daran Mountain) and lowest point is 2150m (near to the Askaran village). The average slope of the basin is 15.5% and slope decries to the east. Climate of basin is Semi-arid according to Demartan index.  Thirteen geomorphologic quantitative variances have been calculated in all sub-basins. These variants belong to the flood-density of streams, geometry of basin, height and climatologically variants of sub basins. For flood estimate we used SCS method in various return periods and cluster analyses by hierarchical and fuzzy method.   Steps for doing methods include:  1- Data selection and estimating (flood-density of streams, geometry of basin, height and climatologically variants of sub basins)  2- Standardizing data (by z-score methods)  3- Suitable algorithm selection for analysis (hierarchical and fuzzy methods) Fuzzy Algorithm  Fuzzy logic is a form of many-valued logic. it deals with reasoning that is fixed or approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific functions.    3- Discussion  We used cluster analysis with Hierarchical method and Fuzzy method for dividing sub basins which belong to homogeneous groups. Using hierarchical method and using ward method was gained the best division. Sub-basins are classified into tree similar classes. Only two groups with similar approximate Membership coefficients were divided by Fuzzy method. In order to achieve better result in this method, variants were experienced in four groups by Fuzzy method and were achieved better result. Membership coefficients of sub-basins were increased in each group. In order to separate sub-basins of view intensity of flood, was used design storm for various return period, in both kinds of cluster method (Fuzzy and Hierarchical method), sub-basins were divided into three similar groups of sub-basins. Results specified that separating sub- basins was similar of view flood peak by Fuzzy and Cluster methods. Rightness of classification was proved by discriminate Analysis. The data demonstrated correct results in both kinds of cluster.  Theoretical classification of sub-basins was done in three identical groups in view point of flood potential. These outcomes were similar to obtained clusters in quantitative analytic method.   4- Conclusion  1- Using the Hierarchical and Fuzzy methods for clustering sub basins in point of view thirteen geomorphologic variables have revealed that Hierarchical method can gain a better clustering for sub-basins.  2- The Hierarchical and Fuzzy methods for clustering sub basins in point of view flood-intensity have indicated both methods achieved to similar cluster and rightness of classification was proved by discriminate analysis  3- By clustering variants in fit groups we can improve conclusions of clustering.  4- Theoretical classification of sub-basins can be a complementary method for comparison and validation of statistical clustering methods.  Key words: cluster analysis, Fuzzy cluster, geomorphologic variants, Karvan basin, and Homogeneous sub-basins.   Refrences  Abdi, Parviz. (2007). 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A. of geomorphology, University of Tarbiat Moallm Sabzevar, Sabzevar, Iran. |