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Article . 2023
License: CC BY
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Indian Journal of Environment Engineering
Article . 2023 . Peer-reviewed
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Modelling Reaeration Coefficient of Stream using Regression Analytical Method - A case of Mmubete Stream, Rivers State Nigeria

Authors: Engr. Akatah, B.M.; Engr. Izinyon, O.C.; Engr. Dr. Gwarah, L.S.;

Modelling Reaeration Coefficient of Stream using Regression Analytical Method - A case of Mmubete Stream, Rivers State Nigeria

Abstract

Surface water pollution is a major problem/ occurrence in the Niger Delta region of Nigeria. Mmubete stream is never an exception. Mmubete stream is significant to the people of Rivers State owing to its usefulness in terms fishing activities and domestic usage. The modelling of Mmubete stream using regression analytical method with emphasis on incorporating mixing properties of stream in stream reaeration prediction was carried out. Water samples were collected and analysed for dissolved oxygen (DO) and temperature. The hydrodynamic data (depth, velocity, surface area, kinematic viscosity and dispersion) of the stream were measured while re-aeration coefficient using empirical models developed using regression analytical approach was determined. The results revealed that the field reaeration coefficient of Mmubete stream ranges from 2.4432d-1 to 3.7568d-1 in the wet season and 0.96d-1 to 2.712d-1 in the dry season. The reaeration coefficient of the stream ranges from 1.983d-1 to 3.088d-1 using the model 1 for the prediction, 1.983d-1 to 3.5065d-1 using the model 2 and 3.0221d-1 to d-1 4.1817 using the model 3. The R2 of the models are 0.934, 0.934 and 0.998 for models 1, 2 and 3 respectively and the standard errors are 0.11135, 0.549694 and 0.022008 for model 1,2 and 3 respectively. The models developed are reliable considering the root mean square and standard error values.

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Keywords

Modelling, Stream, Reaeration, Regression Analysis, Hydrodynamic Data.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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