By Purna Nayak
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Water Resources Research. Vol. 10, pp2217-2725. Environmental Systems Research Institute (ESRI). 2010. ArcInfo ArcMap. Redlands, CA. Gabriele, H. , and F. E. Perkins. 1997. ” Journal of Environmental Engineering. pp61-69. Garren, D. C. 1992. ” Journal of Water Resources Planning and Management. Vol. 118, No. 6. pp654-669. Hocking, R. R. 1996. Methods and Applications of Linear Models. Wiley and Sons, New York, NY. , J. M. Ver Hoef, K. Krivoruchko, and N. Lucas. 2001. Using ArcGis Geostatistical Analyst.
Fig. 24. 233 Table 9. Factor analysis From these results, the variables high in loadings on a particular factor would be those which are highly correlated with one another, but which have little or no correlation with the variables loading highly on the other factors. The negative loading variable has a meaning opposite to that of the factor. The size of the loading is an indication of the extent to which the variable correlates with the factor. Tools for Watershed Planning – Development of a Statewide Source Water Protection System (SWPS) 31 Limitations, and Discussion of Results The limitations with this study can be attributed to the number of gauges used and the variables used to predict stream flow.
A value of 0 for R2 means that none of the variance in the measured data is replicated by the model, and value 1 means that all of the variance in the measured data is replicated by the model. The fact that only the spread of data is quantified which is a major drawback if only R2 is considered. , 2005). 44 Water Resources Management and Modeling 2 R i 1 (simulatedi simulatedaverage )(measuredi measuredaverage ) n n 2 2 i 1 (simulatedi simulatedaverage ) i 1 (measuredi mesuredaverage ) n 2 (1) Function bR2 is defined as coefficient of determination R2 multiplied by the absolute value of the coefficient (slope) of the regression line (b) between simulated (S) and measured (M), with treatment of missing values (2).