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Grouping of Significant Geomorphic Parameters using Multivariate Technique

Author Affiliations

  • 1Deptt. of SWE, CTAE, MPUAT Udaipur, Rajasthan, India
  • 2Deptt. of SWE, CTAE, MPUAT Udaipur, Rajasthan, India
  • 3Deptt. of SWE, CTAE, MPUAT Udaipur, Rajasthan, India
  • 4Deptt. of SWE, CTAE, MPUAT Udaipur, Rajasthan, India
  • 5Deptt. of Agriculture Stat and Computer Applications, RCA, MPUAT Udaipur, India

Res. J. Recent Sci., Volume 5, Issue (11), Pages 32-38, November,2 (2016)

Abstract

The hydrologic modelling play vital role in study of the hydrological behaviour of any watershed. The dimension reduction technique like Principal Component Analysis (PCA) which uses an orthogonal transformation is used in this study. The PCA technique has been applied in upper and middle sub basins of Godavari river basins for 11 selected watersheds, Maharashtra (India). For grouping geomorphic parameters on the basis of their significant correlations13 dimensionless geomorphic parameters are considered. PCA clearly shows that first two PC are strongly correlated among some geomorphic parameters. The results show that the 3rd PC is not showing strong correlation with any parameter but shows moderate correlation with Lb/Lw. The result clearly reveals that, due to poor correlation of the hypsometric integral and main stream channel slope with others could not be grouped with any of the component. The PC loading matrix which is obtained from finally selected 11 parameters correlation matrix, clearly showed first three component gives 94.283% explained variance. Hence it is concluded that PCA is very effective and useful tool to screen out the insignificant parameters for watersheds hydrologic behavioural study such as runoff and sediment yield modelling.

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