Incorporation of Bootstrapping Needful for Sensitivity Analysis: An Eudiometric Theoretic-Approach to Modelling the Assimilative Capacity of a River
The mathematical mechanics of dissolved oxygen (DO) adsorption and subsequent desorption in an effluent-laden water body have hitherto received little attention. The use of hat matrix and bootstrapping approaches to explore the phenomenon of chemical adsorption and desorption of DO at the molecular level in a polluted waterbody has not been adequately investigated, despite the current state of play in this field reflecting the use of many analyses. This study aims to cast virtual spectrum rays on pollutant loadings in a water body using a matrix projector called H-hat (H). unravelling the chemistry and biology of dissolved oxygen gravitation towards elements of wastewater contaminants in the process This method is based on the ordinary least squares method for multivariate linear regression. A mathematical physics examination of the phenomenon backs up the proposed strategy. The means and variances of regression parameters, as well as the confidence intervals of parameter point estimates, were calculated using bootstrapping. The tricking approach used aided the creation of extreme dissolved oxygen values, and therefore the river’s supremum and infimum assimilative capacity, which varies with effluent loading intensity and season of the year (rainy, dry, and harmattan seasons). The results of bootstrapping revealed that assimilative capacity differed significantly from the values detected by point estimates of regression parameters, implying that tricking regression parameters fine-tunes the regression model and thus the value of assimilative capacity through necessary model parameter adjustments. The findings of this study eliminate the necessity for the time-consuming direct measurement of dissolved oxygen in dirty water with a eudiometer. An elegant strategy for crossing the stream when it is shallowest was created in this research. The strategy is thought to be a significant advance over prior approaches that appeared to drag.
Author (S) Details
C. M. Chiejine
Department of Electrical/Electronic Engineering, Delta State Polytechnic, Ogwashi-Uku, Delta State, Nigeria.
A. C. Igboanugo
Department of Production Engineering, University of Benin, Benin City, Edo State, Nigeria.
L. I. N. Ezemonye
Department of Animal and Environmental Biology, University of Benin, Benin City, Edo State, Nigeria.
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