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Information Journal Paper

Title

APPLICATION OF CLUSTER ANALYSIS FOR ESTIMATING SNOW DEPTH (CASE STUDY: SAMSAMI BASIN)

Pages

  25-37

Abstract

 To collect complete data of snow depth from an area, intensive scale of spatial distribution of measurement is needed. But, difficulties are involved in measuring snow depth directly. Because of these difficulties, methods for predicting snow depth should be developed as alternative approaches.In the current research, an area of 5.22 km located in Samsami Basin with an intensive data base of 258 measured points is studied to develop an alternative method. The CLUSTER ANALYSIS model was applied to estimate snow depth for unobserved points in two models. The first model was applied to cluster the snow depth considering parameters including elevation, index of wind shelter and aspect using LINEAR REGRESSION. The second model was used by Fisher's discriminate analysis. To do this, DISCRIMINATE FUNCTIONs were used as estimator of snow depth. Statistical analyses showed that for elevation less than 2767 m, 61% of data variations were modeled using the first model. But, for the higher elevations, this model was unable to predict the unobserved data. However, the second model predicted 53% of data.

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    APA: Copy

    SHARIFI, M.R., AKHONDALI, A.M., PORHEMMAT, J., & MOHAMMADI, J.. (2008). APPLICATION OF CLUSTER ANALYSIS FOR ESTIMATING SNOW DEPTH (CASE STUDY: SAMSAMI BASIN). AGRICULTURAL RESEARCH, 7(4 (B)), 25-37. SID. https://sid.ir/paper/84786/en

    Vancouver: Copy

    SHARIFI M.R., AKHONDALI A.M., PORHEMMAT J., MOHAMMADI J.. APPLICATION OF CLUSTER ANALYSIS FOR ESTIMATING SNOW DEPTH (CASE STUDY: SAMSAMI BASIN). AGRICULTURAL RESEARCH[Internet]. 2008;7(4 (B)):25-37. Available from: https://sid.ir/paper/84786/en

    IEEE: Copy

    M.R. SHARIFI, A.M. AKHONDALI, J. PORHEMMAT, and J. MOHAMMADI, “APPLICATION OF CLUSTER ANALYSIS FOR ESTIMATING SNOW DEPTH (CASE STUDY: SAMSAMI BASIN),” AGRICULTURAL RESEARCH, vol. 7, no. 4 (B), pp. 25–37, 2008, [Online]. Available: https://sid.ir/paper/84786/en

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