Prediction of re-aeration coefficient of rivers from streamflow characteristics
| dc.contributor.author | Chimezie, Udochi Ijeoma. | |
| dc.date.accessioned | 2025-10-30T13:27:29Z | |
| dc.date.available | 2025-10-30T13:27:29Z | |
| dc.date.issued | 2023-04 | |
| dc.description | Master’s thesis in water resources engineering. It contains mathematical equations, pictures, graphs and tables. | |
| dc.description.abstract | In this study, a new model, designated as the N-Model, was developed to predict the re-aeration coefficient of rivers using streamflow characteristics. Re-aeration is an important phenomenon that sustains dissolved oxygen levels in receiving waters to support aquatic life and natural selfpurification of streams and rivers. Development of the N-Model was done for the sole purpose of getting a more accurate model that could easily be applied with no heavy laboratory work in order to be put into application on site. The performance of the N-Model is compared against various existing empirical models like O'Connor, Parkhurst, Churchill, Krenkel, Thackston, and Owen. Data collection was done from the following rivers: Otammiri, Kaduna, Adada, Oshika lake and Atuwara. Using O'Connor's model, the re-aeration coefficient of the Otammiri River was found to be 0.0753 with a very high correlation coefficient of 99.2%. The N-Model predicted the value for Otammiri River to be 0.076 with an accuracy of 98.4%. This was determined by comparing the predicted value of the N-Model with observed data, where the minimal difference between the two (0.076 vs. 0.075) indicated that the model was highly effective in estimating the re-aeration coefficient. The model by Parkhurst produced a coefficient of 0.078 while other models like the ones by Churchill and Owen showed much higher discrepancies with percentage errors of over 70%. Across all rivers, the N-Model demonstrated strong predictive accuracy, with an overall correlation coefficient of 98.9% and a low standard error of less than 4%. However, other models like Churchill and Krenkel give very small correlation coefficients often less than 25%, indicating that the N-Model offers a reliable and efficient alternative for estimating re-aeration coefficients from streamflow characteristics in varying environments. | |
| dc.identifier.citation | Chimezie, U. I. (2023). Prediction of pe-aeration coefficient of rivers from streamflow characteristics. {Unpublished Master's Thesis}. Federal Universiity of Technology, Owerri. | |
| dc.identifier.uri | https://repository.futo.edu.ng/handle/20.500.14562/2246 | |
| dc.language.iso | en | |
| dc.publisher | Federal University of Technology, Owerri. | |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject | Re-aeration coefficient | |
| dc.subject | rivers | |
| dc.subject | models stream flow data | |
| dc.subject | performance. | |
| dc.subject | department of civil engineering\ | |
| dc.title | Prediction of re-aeration coefficient of rivers from streamflow characteristics | |
| dc.type | Master’s Thesis |