http://117.252.14.250:8080/jspui/handle/123456789/2665 2026-04-21T22:12:31Z 2026-04-21T22:12:31Z Seth, S. M. Goel, N. K. http://117.252.14.250:8080/jspui/handle/123456789/2672 2023-04-12T18:29:17Z 1983-01-01T00:00:00Z

DP-7 : Polynomial regression Seth, S. M.; Goel, N. K. For any non linear function Y = f(x) regression may be obtained by fitting a polynomial. The general form of the polynomial regression is as given under: Y=a0+a1X + a2X²+...............................................amXm+E Where : Y is the dependent variable and a0, a1, ............am are the regression coefficients. The documentation of the computer programme for polynomial regression includes the listing of the source file, data file and output file with test data and example calculations. The details of various statistics given in the programme output have also been given in the documentation.

1983-01-01T00:00:00Z Seth, S. M. Goel, N. K. http://117.252.14.250:8080/jspui/handle/123456789/2671 2023-04-12T18:29:08Z 1983-01-01T00:00:00Z

DP-6 : Multiple linear regression Seth, S. M.; Goel, N. K. The association of three or more variables can be Investigated by multiple linear regression and correlation analysis. The derivation of relationships among hydrologic variables is of importance for the transfer of information from few gauged stations to many ungauged stations. The general form of the multiple linear regression is X1= B1 + B2X2+B3X3+……..+BmXm+e where, X1 is dependent variable and X2, X3, .........Xm are independent variables. E is the error term. In the documentation, listing of the source programme for multiple linear regression analysis, input data file and output file is given with test data and example calculations. In the programme the selection of different sets of independent variables and designation of dependent variable can be made as many times as desired. The programme calculates means, standard derivations of dependent and independent variables, correlation coefficients between dependent and independent variables, regression coefficients, standard error of regression coefficients, computed t-values, intercept, multiple correlation coefficient, standard error of estimate, analysis of variance for multiple regression and table of residuals.

1983-01-01T00:00:00Z Seth, S. M. Palaniappan, A. B. http://117.252.14.250:8080/jspui/handle/123456789/2670 2025-10-22T05:20:48Z 1982-01-01T00:00:00Z

DP-5 : Flood routing (Muskingum cunge procedure) Seth, S. M.; Palaniappan, A. B. Flood routing in a natural river is complicated by the presence of irregularities of cross-section and by the presence of lateral flow. It is now possible to quantify the effect of irregularities in the width and the corresponding effect of storage caused by them. These irregular sub reaches act as a series of reservoirs and provide attenuation. Cunge brought out certain salient features of Muskingum method and stated that the attenuation seen in the routed flow using Muskingum model is just because of the numerical error and not due to the ability of the model. He showed that the finite difference approximation used in Muskingum method is also an approximation of a diffusion equation using Taylor series expansion. Cunge has developed a method of estimating the attenuation parameter using average width and slope of a river. R.K.Price worked further and improved it to include the variations in the width and slope. The routing parameter x is related to the attenuation parameter. Based on the value of attenuation parameter and wave speed 'C' using the recurrence relation available in Flood Studies Report, Vol.III of National Environmental Research Council, London, a FORTRAN programme capable of routing the flow was developed in National Institute of Hydrology, Roorkee with following features in addition to routing: (a) finds the attenuation parameter given physical features viz. the widths, slopes, and reach lengths for a given discharge, (b) the lateral flow is obtained as the difference in observed inflow and outflow quantities and distributed as per the ordinates of either inflow or outflow as opted, and (c) the results are plotted in addition to Printing of the discrete value. The programme has been explained fully in the documentation with flow Chart. The input specifications and the output descriptions are also given. An example using the data of a flood in the reach between Mortakka and Garudeshwar on the river Narmada is also given in Appendix 1.

1982-01-01T00:00:00Z Seth, S. M. Nirupama, P. http://117.252.14.250:8080/jspui/handle/123456789/2669 2023-04-21T19:01:33Z 1983-01-01T00:00:00Z

DP-4 : Ordering the series and interpolation Seth, S. M.; Nirupama, P. The documentation for ordering the series describes the comparative studies carried out using four subroutines available in the literature. The comparison is made on the basis of compilation time, run time and memory requirements of the programme. The mean and standard deviation of run tines are also compared. The test data used are ten different series of 600 real numbers each generated by a random number generation subroutine. The study shows that the subroutine ORDER 2 which uses the principle of 'division and comparison' takes minimum average run time. Also it requires less total time including compilation and execution times. The input description and the subroutine listing are given in the documentation. The documentation for interpolation describes the use of three subroutines. The methods employed in these subroutines are (1) spline fit, 2) second order parabolic, and (3) Lagrange's interpolation. No internal data storage is required for any of the three subroutines listed in the documentation. Input data requirements are specified. Example input and output of the subroutines are listed in the documentation.

1983-01-01T00:00:00Z